NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Variability of Photovoltaic 

Power in the State of Gujarat 

Using High Resolution Solar 

Data

M. Hummon, J. Cochran, A. Weekley, A. Lopez, 

J. Zhang, and B. Stoltenberg

National Renewable Energy Laboratory

B. Parsons

Evergreen Renewable Consulting

P. Batra

Central Electricity Authority, India

B. Mehta and D. Patel

Gujarat Energy Transmission Corporation Ltd.

Technical Report

NREL/TP-7A40-60991

March 2014
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Variability of Photovoltaic 

Power in the State of Gujarat 

Using High Resolution Solar 

Data

M. Hummon, J. Cochran, A. Weekley, A. Lopez, 

J. Zhang, and B. Stoltenberg

National Renewable Energy Laboratory

B. Parsons

Evergreen Renewable Consulting

P. Batra

Central Electricity Authority, India

B. Mehta and D. Patel

Gujarat Energy Transmission Corporation Ltd.

Prepared under Task No. IGIN6300

Technical Report

NREL/TP-7A40-60991

March 2014
NOTICE

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or favoring by the United States government or any agency thereof. The views and opinions of authors expressed 

herein do not necessarily state or reflect those of the United States government or any agency thereof.

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
iii

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Acknowledgements

The authors are greatly indebted to thoughtful reviews and comments from Surendar Kumar 

Negi, Gujarat Energy Transmission Corporation Ltd.; Elena Berger, U.S. Department of Energy; 

Shannon Cowlin, Asian Development Bank; Anish De, Mercados Energy Markets India Private 

Limited; Anjan Bose, Washington State University; Ranjit Deshmukh of University of 

California, Berkeley, and Lawrence Berkeley National Laboratory; and Doug Arent, Jeffrey 

Logan, Kara Clark, Nate Blair, and Trieu Mai of the National Renewable Energy Laboratory 

(NREL). The authors also wish to thank Sarah Booth and Melissa Butheau (NREL) for providing 

valuable input and comments during the analysis and production process. Any errors or 

omissions are solely the responsibility of the authors.

This analysis was funded by the U.S. Department of Energy’s Office of Energy Efficiency &

Renewable Energy and developed in coordination with India’s Ministry of New and Renewable 

Energy under the U.S.-India Energy Dialogue’s New Technology and Renewable Energy 

Working Group.
iv

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List of Abbreviations and Acronyms

AC alternating current

ci clearness index

cpi clear power index

DC direct current

DFI diffuse irradiance

DNI direct normal irradiance

GETCO Gujarat Energy Transmission Corporation

GHI global horizontal irradiance

GW gigawatt

GWDC gigawatt direct current

km kilometer

km2 square kilometer

kWh kilowatt hour

MW megawatt

MWDC megawatt direct current

NLDC National Load Dispatch Centre

NREL National Renewable Energy Laboratory

PPA power purchase agreement

PV photovoltaics

REMC Renewable Energy Management Centres 

SCADA supervisory control and data acquisition

SIA sub-hour irradiance algorithm

SLDC State Load Dispatch Centre
v

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Executive Summary

India has ambitious goals for high utilization of variable renewable power from wind and solar, 

and deployment has been proceeding at a rapid pace. The western state of Gujarat currently has 

the largest amount of solar capacity of any Indian state, with over 855 megawatts direct current 

(MWDC)

1

among plants above 1 MWDC in size. Combined with over 3,240 megawatts (MW) of 

wind, variable generation renewables comprise nearly 18% of the electric-generating capacity in 

the state. The Central Electricity Authority has projected these wind and solar capacities will 

more than double by 2017.2 

With high penetration levels of wind and solar, system operators must have access to additional 

resources that can help balance the net-load variability (load minus wind and solar output) and 

carry adequate reserves to respond to the combination of load and variable generation forecast 

errors. To assess the adequacy of balancing resources, and to evaluate operational practices to 

access these resources, system operators and planners typically perform grid integration studies. 

Key to informative analysis is accurate representationspatially and temporallyof the power 

variability and uncertainty of solar and wind generation. This report focuses on the solar 

characteristics needed for a grid integration analysis, which would also include information on 

load, wind, and conventional generation. 

A new historic 10-kilometer (km) gridded solar radiation data set capturing hourly insolation 

values for 2002-2011 is available for India.3 The authors apply an established method for 

downscaling hourly irradiance data to one-minute irradiance data at each photovoltaic (PV)

power production location for one year—2006.4 Using this data, the authors quantify solar 

production at locations reflecting six scenarios: baseline, totaling 1.9 gigawatts direct current 

[GWDC]) and five possible expansion scenarios, each adding 500-1,000 MW of solar capacity, to 

yield a total installed solar capacity of 2.4 GWDC and 2.9 GWDC. The scenarios are:

1. Baseline: existing and planned solar generation, totaling 1.9 GWDC

2. Charanka: expansion at an existing, single solar park; represents the most geographically 

concentrated scenario, 1.0 GWDC

3. Seven utility PV locations: expansion at the seven best, developable sites, distributed 

throughout the state, 1.0 GWDC

4. Kutchh: expansion in the Kutchh region of Gujarat, distributed across the region, 1.0 

GWDC

 

1 Direct current (DC) ratings refer to the capacity of the photovoltaic panels under standard conditions: 1,000 

watts/square meter, 25°C. Alternate current (AC) ratings refer to the peak power output of the inverter, which 

includes system losses and DC to AC conversion losses.

2 Large-Scale Grid Integration of Renewable Energy Sources—Way Forward. Central Electricity Authority, 2013. 

www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf. 3 “India Solar Resource Data: Hourly.” National Renewable Energy Laboratory, 2013. 

http://www.nrel.gov/docs/fy14osti/61121.pdf, http://rredc.nrel.gov/solar/new_data/India/nearestcell.cgi. 4 A single year of sub-hour data captures the seasonal and time of day variability; however multiple study years 

would be needed to understand the long-term economics of a PV plant at a particular location.
vi

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

5. Narmada Canal: expansion above the canal, evenly distributed across its length 0.5 

GWDC

6. Sixteen Cities rooftop PV: expansion across rooftops in the 16 largest cities, evenly 

distributed across the cities; represents the most geographically diverse scenario, 1.0 

GWDC.

Three of the expansion scenarios (Charanka, Kutchh, and Narmada Canal) were suggested by the 

Gujarat Energy Transmission Corporation (GETCO). The other two scenarios were selected to 

reflect broader geographic diversity.

The objective of this report is to characterize and contrast the intra-hour variability of these six 

PV generation scenarios. The report statistically analyzes one year’s worth of power variability 

data, applied to both the baseline and expansion scenarios, to evaluate diurnal and seasonal 

power fluctuations, different timescales of variability (e.g., from one to 15 minutes), the 

magnitude of variability (both total megawatts and relative to installed solar capacity), and the 

extent to which the variability can be anticipated in advance. The paper also examines how 

GETCO and the Gujarat State Load Dispatch Centre (SLDC) could make use of the solar 

variability profiles in operations and planning.

One factor inherent to grid balancing challenges associated with increased solar deployment is 

the ramp rate, which is the sustained rate of power increase or decrease over time.

5 Solar power 

ramps result from both the daily solar path and cloud patterns that decrease the incident solar 

radiation on the PV panels. Quantifying total MW per minute ramp rates allows system operators 

and planners to assess balancing options.

This analysis quantifies the relatively simple concept that the total magnitude of solar power 

ramping goes up with increased solar capacity. Simply put, total ramping in the baseline scenario 

of 1.9 GWDC is less than total ramping in the baseline plus expansion scenario of 2.4 GWDC, 

which is less than the four other baseline plus expansion scenarios totaling 2.9 GWDC. The 

dominant cause of this correlation is ramping due to sunrise and sunset, which occurs over a 

short, predictable period of time. During the monsoon season, clouds reduce the sunrise and 

sunset ramp rates, and decrease the peak solar output by 20-35%. Figure ES-1, the aggregate 

power output from the baseline plus each expansion scenario in monsoon (left) and dry (right) 

seasons, illustrates these results. 

 

5 This change in power over time is typically expressed in MW over some time scale of interest, for example five 

minutes.
vii

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Figure ES-1. Aggregate power output of baseline plus expansion scenarios on July 23, 2006

(monsoon), and Oct. 12, 2006 (dry season)

This analysis also quantifies that aggregate power ramps from solar expansion scenarios are less 

dramatic as the geographic spread of deployment (spatial diversity) is increased. In contrast to 

using total magnitude ramp rates, illustrating the more complex effects of geographic diversity 

from alternative solar expansion scenarios is better assessed by normalizing ramp rates to the 

total nameplate rating. 

As Figure ES-2a illustrates, the five-minute ramps normalized as a percentage of installed solar 

capacity are relatively constant across the baseline and expansion scenarios during the dry 

season, but vary based on time of day. The smallest ramps occur during the midday of the dry 

season, with the sun high in the sky and very few clouds. While observable, the ramp rate 

differences between scenarios are small because the East-West and North-South impacts on solar 

path are not large within Gujarat’s boundaries. 
viii

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure ES-2. The dry season (a) and monsoon season (b) 95th-percentile five-minute ramp (percent 

of scenario installed DC solar capacity) during each time of day: sunrise, morning, midday, 

afternoon, and sunset

The monsoon season (Figure ES-2b) shows a different time of day pattern than the dry 

seasonthe largest ramps occur during midday, when there are many clouds that change the 

solar power production. The effect of geographic diversity becomes more noticeable when cloud 

coverage contributes to solar ramping. In this case, geographically diverse locations, such as 

baseline plus utility PV at seven locations or rooftop PV, experience lower absolute ramps than 

similarly sized scenarios. The baseline plus Charanka scenario (2.9 GWDC), which concentrates 

new solar generation in a single location, has 30% and 7% greater ramps in the monsoon and dry 

seasons, respectively, over the baseline plus rooftop PV in 16 cities (2.9 GWDC). These increases 

may be significant if added requirements for load and conventional generation to provide 

balancing services are costly.

The report also presents analysis showing the greatest difference between the monsoon and dry 

seasons is seen in the average unpredicted one-minute ramps, which demonstrates that dry 

season variability conditions driven by solar path alone persist for longer periods than monsoon 

season variability conditions where clouds impact solar output. In other words, it is harder to 

predict short-term variability in the monsoon season than in the dry season.

The impact of solar variability quantified in this study can be managed through targeted changes 

to system operations and planning. Because most of the solar ramping is based on known 

changes due to sunrise and sunset, the Gujarat SLDC may be able to schedule its resources 

accordingly. The greater periods of uncertainty stem from cloud-based variability. To address 

this impact, Gujarat, in coordination with the central government authorities, is in the process of 

assessing and strengthening its suite of tools to anticipate and mitigate this variability, which 

include stronger grids, advanced forecasting (day ahead and real-time), improved scheduling, 

and market reforms.
ix

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

This work brings out several salient features that are important when evaluating potential future 

PV renewable generation and its potential grid integration impacts on the power system, 

including:

• The diurnal and seasonal power profiles and variability statistics for the existing and 

planned 1.9 gigawatts (GW) of solar are characterized using 2006 historic solar resource 

data. The geographic spread of this baseline is relatively broad. Power output is raised 

slightly if a portion of the plants is assumed to incorporate single-axis tracking, but the 

statistics of variability are largely unaffected by tracking. 

• The expansion scenarios of 500 to 1,000 MW increase nearly proportionally the absolute 

magnitude of the solar variability. The differences in geographic density of the expansion 

scenarios affect variability, but when combined with the geographically diverse baseline 

scenario and normalized to the total nameplate DC capacity, the differences among 

scenarios are small. 

• Results illustrate the power ramp rate differences between a single small plant, a very 

large plant, several spread out central plants, and rooftop distributed deployments. The 

least geographically diverse, baseline plus Charanka scenario, has 30% and 7% greater 

midday ramps in the monsoon and dry seasons, respectively, over the more 

geographically diverse baseline plus rooftop PV in 16 cities scenario.

• In evaluating expansion plans, system planners need to consider other factors related to 

location, such as access to uncongested transmission capacity, in addition to variability 

differences. If solar deployment across more Indian states is considered, and state-to-state 

grid cooperation is increased, the beneficial impacts of geographic diversity should be 

larger and would be worth re-evaluating.

• During the monsoon season, individual plant variability can be quite large due to cloud 

passage, but the aggregated power across all plants shows less volatility. Due to the broad 

and general nature of the storms, planning and operating the system using a derating of 

the solar capacity may help systems operations. Further research could assess whether

this implication could be applicable to other regions of the world with similar monsoonal 

climatology and where solar power is being deployed or contemplated.

• The study shows that much of the diurnal solar variability is based on a known solar path, 

illustrating a large amount of the PV-imposed needs for grid flexibility are known in 

advance, which facilitates grid operational day-ahead scheduling.

• The variability shows strong seasonal characteristics. Generally, absolute variability is 

higher during dry seasons during known sunrise and sunset ramps. Variability relative to 

output is higher during the monsoon season at midday, when the region experiences a 

dramatic decrease in solar power output due to periodic (and relatively unpredictable) 

cloud passage. 

• Diurnal and seasonal variability characteristics can be important to grid practices. Known 

variability implies ramping resources can be confidently quantified and potentially 

scheduled. Unpredictable variability statistics can also be quantified, but may require 

partially loaded quick ramp or quick start/stop resources to respond. 
x

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Finally, it is recognized that solar profiles are only part of the renewable energy integration study 

needs. The broader context calls for similar evaluation of wind power deployment scenario 

variability, which will be undertaken by the authors later in 2014. The results of this report 

should not be taken independently, as only studies of the full integrated electric system, 

including conventional generator capabilities, load variability, operational practice reform, and 

assessment of system-level mitigation measures will allow system planners and operators to 

examine and address challenges imparted by large-scale variable generation futures.
xi

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Table of Contents

Acknowledgements ................................................................................................................................... iii

List of Abbreviations and Acronyms ....................................................................................................... iv

Executive Summary.................................................................................................................................... v

List of Tables ............................................................................................................................................. xii

List of Figures ........................................................................................................................................... xii

1 Introduction........................................................................................................................................... 1

2 Solar Power Scenarios......................................................................................................................... 4

2.1 Existing and Expected Locations..................................................................................................... 5

2.2 Expansion Locations........................................................................................................................ 7

2.2.1 Charanka.............................................................................................................................. 8

2.2.2 Seven Utility Photovoltaic Locations.................................................................................. 9

2.2.3 Kutchh Region................................................................................................................... 10

2.2.4 Narmada Canal .................................................................................................................. 11

2.2.5 Sixteen Cities: Rooftop PV................................................................................................ 12

2.3 Summary of Scenarios................................................................................................................... 13

3 Sub-Hour Solar Power Data............................................................................................................... 14

3.1 Satellite Irradiance and Meteorological Data................................................................................. 15

3.2 Sub-Hour Irradiance Algorithm..................................................................................................... 16

3.2.1 Spatial and Temporal Variability Relationship ................................................................. 16

3.2.2 Modeling the Temporal Variability Classes...................................................................... 18

3.2.3 Reduction in Variability with Increasing Photovoltaic Plant Area.................................... 21

3.2.4 Converting Irradiance Data to Alternate Current Power Output ....................................... 22

3.3 Summary........................................................................................................................................ 23

4 Solar Power Variability....................................................................................................................... 24

4.1 Scenario Power Production and Capacity...................................................................................... 24

4.2 Variability of the Scenarios ........................................................................................................... 29

4.3 Summary........................................................................................................................................ 38

5 Actions to Address the Variability of Solar Generation ................................................................. 40

5.1 Grid Reinforcement ....................................................................................................................... 40

5.2 System Operations......................................................................................................................... 41

5.2.1 Advanced Forecasting ....................................................................................................... 41

5.2.2 Improved Scheduling......................................................................................................... 42

5.2.3 Ancillary Services.............................................................................................................. 43

5.3 Market Reforms............................................................................................................................. 44

5.4 Summary........................................................................................................................................ 45

6 Summary, Implications, and Next Steps ......................................................................................... 46

6.1 The Need for Similar Wind Resource and Power Profiles ............................................................ 47

6.2 Uses of Renewable Power Variability Profiles.............................................................................. 48

Appendix: Solar Plant Diagrams ............................................................................................................. 50
xii

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List of Tables

Table 1. Allocation of Capacity for Utility PV Plants in the Kutchh Region........................................ 10

Table 2. Capacity, Area, and Density of the Baseline and Expansion Scenarios for Gujarat........... 13

Table 3. Hours of Missing Meteosat Satellite Data ................................................................................ 16

Table 4. Installed Capacity, Annual Power Output, and Capacity Factor for the Expected Location 

Scenariosa ........................................................................................................................................... 25

Table 5. Installed Capacity, Annual Power Output, and Capacity Factor for the Expansion 

Scenarios and the Baseline Scenario (all fixed-axis) Plus Each Expansion Scenarioa .............. 26

Table 6. The Median (a) and 95th Percentile (b) Five-Minute Ramp (absolute magnitude, percent of 

DC solar capacity, and clear power index) by Season ................................................................... 35

List of Figures

Figure ES-1. Aggregate power output of baseline plus expansion scenarios on July 23, 2006 

(monsoon), and Oct. 12, 2006 (dry season)..................................................................................... vii

Figure ES-2. The dry season (a) and monsoon season (b) 95th-percentile five-minute ramp (percent 

of scenario installed DC solar capacity) during each time of day: sunrise, morning, midday, 

afternoon, and sunset ....................................................................................................................... viii

Figure 1. Annual average of GHI for India ................................................................................................ 5

Figure 2. Baseline scenario PV plant locations in Gujarat, India........................................................... 6

Figure 3. Baseline scenarios with approximately 200 MW of single-axis tracking (a) and 

approximately 450 MW of single-axis tracking (b)............................................................................ 7

Figure 4. Charanka scenario is 1.0 GW of additional capacity at Charanka Solar Park (yellow) with 

the baseline scenario (orange)............................................................................................................ 9

Figure 5. Seven utility PV locations scenario (yellow) each with 143 MW PV plants with the 

baseline scenario (orange)................................................................................................................ 10

Figure 6. Kutchh scenario (yellow) consists of six utility PV plants ranging from 100 to 200 MW 

with the baseline scenario (orange) ................................................................................................. 11

Figure 7. Narmada Canal scenario (yellow) with 500 MW distributed along the Narmada Canal in 

Gujarat with the baseline scenario (orange).................................................................................... 12

Figure 8. Sixteen cities: rooftop PV scenario (yellow) consists of 62.5 MW in each of 16 cities with 

the baseline scenario (orange).......................................................................................................... 13

Figure 9. Simultaneous irradiance measurements from two sites 70.4 km apart at two time scales: 

tstep = 1 minute (line) and satellite irradiance values, tstep = 60 minutes (dotted line with 

circles). The site near Boulder, Colorado, is located at 39.911_N -105.235_W (gray), and the 

site near Golden, Colorado, is located at 39.742_N -105.180_W (black)....................................... 15

Figure 10. GHI for the ground-measured data and calculated for the clear sky on Feb. 24, 2008, at 

NREL’s Solar Research Radiation Laboratory in Golden, Colorado (a). Dividing the observed 

GHI by the clear sky GHI results in the clearness index (b). ......................................................... 17

Figure 11. Spatial clearness index patch (a) and time series measured at the starred location for 

the same hour of time (b). Data shown are from the Measurement and Data Instrumentation 

Center at NREL, in Golden, Colorado............................................................................................... 17

Figure 12. Classes of temporal variability (from SIA) ........................................................................... 18

Figure 13. Workflow for producing modeled ci values at a single location........................................ 18

Figure 14. Weighted mean and standard deviation of the patch ci values determines the 

probability distribution of temporal classifications for that patch................................................ 19

Figure 15. Clearness index ramp distributions for measured data with temporal variability 

classification of Class 0, I, II, and III ................................................................................................. 20

Figure 16. Time series of one-minute GHI before and after applying the low-pass filter for a plant 

area of 2.6 km2 .................................................................................................................................... 21

Figure 17. Cut-off time below which there is reduced variability due to the geographic area of the 

plant ..................................................................................................................................................... 22

Figure 18. Power output for a 200 MWDC PV plant with either fixed panel at 17° tilt or a single-axis 
xiii

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tracking panel with no tilt, on Jan. 23-24, 2006, at 23.65° N, 69.55° E in the Kutchh region....... 25

Figure 19. Clear sky and synthetic PV power output, one-minute resolution, for (left) single, 

randomly selected site from the baseline PV scenario, and (right) aggregation of all of the 

baseline PV sites on July 23, 2006, and Oct. 12, 2006.................................................................... 27

Figure 20. Aggregate power output in the baseline and expansion scenarios, separately (top) and 

combined (bottom), on July 23, 2006 (left) and Oct. 12, 2006 (right) ............................................ 28

Figure 21. Color map of power production from the baseline scenario by time of day (vertical axis) 

and time of year (horizontal axis) ..................................................................................................... 29

Figure 22. Five-minute clear power index ramp distribution for each baseline scenario and each 

expansion scenario (separate from the baseline). The “count” axis is on a log scale............... 30

Figure 23. Absolute five-minute (a) and 15-minute (b) solar power ramp distribution for the 

baseline scenario and the baseline scenario plus each of the five expansion scenarios 

(daytime analysis) .............................................................................................................................. 31

Figure 24. Distribution of five-minute clear power index ramps for the baseline scenario, baseline 

plus expansion scenarios, and a randomly selected site from the baseline scenario (1 MW 

fixed-PV plant located at 24.05°, 71.55°)........................................................................................... 31

Figure 25. Comparison of the daytime variability of three scenarios: baseline, Charanka, and 

rooftop PV in 16 cities........................................................................................................................ 32

Figure 26. Definitions for (a) time of day and (b) season for five-minute ramp analysis .................. 33

Figure 27. Example of the distribution of five-minute ramps and the identification of the 50th and 

95th percentile: (a) the baseline scenario, in the dry season, for the distributions of five-minute 

absolute ramps for the sunrise and midday "time of day" periods, (b) the distributions of fiveminute absolute ramps scaled by the installed capacity for the baseline and baseline plus 

Charanka scenarios during midday of the dry season, and (c) the baseline scenario during 

midday, for the dry and monsoon seasons, expressed as a percentage of the clear sky fiveminute ramp. ....................................................................................................................................... 34

Figure 28. The (a) dry season and (b) monsoon season 95th percentile five-minute ramp (percent of 

scenario installed DC solar capacity) during each time of day: sunrise, morning, midday, 

afternoon, and sunset ........................................................................................................................ 36

Figure 29. Illustration of the calculation of unpredicted one-minute solar power variability........... 37

Figure 30. Average minute-to-minute change in solar production, normalized by expected clear 

sky solar production for the baseline scenario plus each expansion scenario. Darker tones 

reflect the incremental addition of ramps from the expansion scenarios. .................................. 38

Figure A-1. Existing solar plants in Gujarat ........................................................................................... 50

Figure A-2. Expected solar plants in Gujarat ......................................................................................... 51
1

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1 Introduction 

India has ambitious goals for high utilization of variable renewable power from wind and solar,

and deployment has been proceeding at a rapid pace. The western state of Gujarat currently has 

the largest amount of solar capacity of any Indian state, with over 855 megawatts (MW) among

plants above 1 MW in size. Combined with over 3,240 MW of wind, variable generation 

renewables are nearly 18% of the electric-generating capacity in the state.

6 The Central 

Electricity Authority has projected these wind and solar capacities will more than double by 

2017.7 

Worldwide, electric utilities and grid operators are concerned by the variable and uncertain 

nature of weather-driven renewable power sourceswind and solar generation output varies 

over time, and this output cannot be predicted with perfect accuracy. Grid operators routinely 

balance electric load (demand) with the suite of dispatchable conventional generators. With high 

penetration levels of wind and solar, system operators must have access to additional resources 

that can help balance the net-load variability (load minus wind and solar output) and carry 

adequate reserves to respond to the combination of load and variable generation forecast errors. 

A variety of approaches to both analyzing and mitigating impacts exist.

8,9

 

India-specific challenges with variable generation are being discussed and addressed in the 

broader context of grid modernization through grid synchronization, strengthening of 

transmission ties, operator practice, and market design.10 Previous collaboration between the 

U.S. Department of Energy’s National Renewable Energy Laboratory (NREL) and the Gujarat 

Energy Transmission Corporation (GETCO)11 discusses the application of grid-operational 

modeling studies to examine challenges and mitigation measures for high future penetrations of 

renewable variable generation in Gujarat. The 2012 effort described the nature of a market-based 

integration study and how this approach, while new to Indian grid operation and planning, is 

necessary to understand how to operate and expand the grid to best accommodate the expansion 

of variable generation. The paper also discussed options in defining a study’s scope, such as data 

granularity, generation modeling, and geographic scope. Finally, the paper explored how 

 

6 Gujarat, like other Indian states, bears primary responsibility for generation-load balance within its boundaries, but 

is connected to the single, India-wide electrical interconnection. 7 Large-Scale Grid Integration of Renewable Energy Sources—Way Forward. Central Electricity Authority, 2013. 

www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf. 8 See, for example, Holttinen, H. et al. Design and Operation of Power Systems with Large Amounts of Wind Power. 

VTT Technology, 2013. www.ieawind.org/task_25/PDF/T75.pdf. 9 Cochran, J., L. Bird, et al. Integrating Variable Renewable Energy in Electric Power Markets: Best Practices from 

International Experience. Golden, CO: National Renewable Energy Laboratory, 2012. 

www.nrel.gov/docs/fy12osti/53732.pdf. 10 Integrating Variable Renewable Energy with the Grid: Lessons from the Southern Region. Mercados Energy 

Markets India PVT Ltd., November 2012. 

www.shaktifoundation.in/cms/uploadedImages/variable%20re%20grid%20integration.pdf. 11 Stoltenberg, B. “Grid Integration Studies and India.” Presented at the 2012 World Renewable Energy Forum.

http://nrelpubs.nrel.gov/Webtop/ws/nich/www/public/Record?rpp=25&upp=0&m=1&w=NATIVE%28%27TITLE_

V+ph+words+%27%27india+grid%27%27%27%29&order=native%28%27pubyear%2FDescend%27%29.
2

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Gujarat's method of grid operation and current system reliability will affect how an integration 

study can be performed. 

Market-based integration studies allow quantifiable evaluation of resourcesand operational 

practices to access these resourcesthat are necessary to maintain reliability under various 

projections of wind and solar penetration. These studies evaluate characteristics and practices,

such as unit commitment and dispatch; the flexibility of existing conventional generators;

quantity and types of reserves; and cooperation between electrical balancing areas. Such studies 

can also examine the effects of modifications to historic practices, including market design, 

ancillary services, wind and solar power forecasts, and demand scheduling and control. Different 

future generation portfolios can be modeled to help guide resource acquisition, including storage

acquisition, and value the provision of flexibility, such as the ability to increase output quickly or 

decrease output to low generation levels.

Key to all these informative analyses is accurate representation of the power variability and 

uncertainty of solar and wind generation. Individual plants show very rapid power changes due 

to cloud passage and wind speed variation. Simply scaling up output patterns from existing 

renewable plants to higher future deployment levels ignores deployment expansion geographic 

diversity effects and significantly overestimates fluctuations and mischaracterizes the magnitude 

and possible costs of the integration challenge.

12 Thus, spatial and temporal diversity must be 

accurately represented to capture renewable power ramps, which are the changes in generation 

output. 

To represent spatial and temporal diversity, system planners typically examine multiple scenarios 

that include alternative plant locations, and use historic, time-synchronized load, wind, and solar 

patterns to capture weather-driven correlations between electric demand and renewable power 

delivery. Recent, advanced variable generation integration studies have highlighted the value of 

examining sub-hourly variability, which significantly increases the complexity in characterizing 

temporal variability.13 

Development of these wind and solar resource databases is nontrivial. In the case of wind, largescale, mesoscale weather models, calibrated with historic weather patterns, are used to predict 

wind speeds at potential generation sites. For solar, historic satellite photos of clouds can be used 

to determine ground insolation values. Recently, NREL completed a new historic hourly solar 

radiation data set capturing hourly insolation values for 2002-2011 for India.

14

The objective of this report is to characterize the intra-hour variability of existing and planned 

solar generation in the state of Gujarat, and of multiple future scenarios of solar generation that 

reflect different plant locations. The authors apply an established method for downscaling hourly 

 

12 Holttinen, H. et al. “Recommendations for Wind Integration.” IEA Wind, October 2012. 

www.ieawind.org/task_25/PDF/WIW12_101_Task%2025_Recommendations_submitted.pdf . 13 “Transmission Grid Integration: Western Wind and Solar Integration Study.” National Renewable Energy 

Laboratory, 2013. www.nrel.gov/electricity/transmission/western_wind.html. 14 “India Solar Resource Data: Hourly.” National Renewable Energy Laboratory, undated. 

http://rredc.nrel.gov/solar/new_data/India/nearestcell.cgi. 
3

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irradiance data to one-minute irradiance data at each PV power production location for one 

year—2006. A single year of sub-hour data captures the seasonal and time of day variability; 

however, multiple study years would be needed to understand the long-term economics of a 

photovoltaic (PV) plant at a particular location. Following this introduction, Section 2 describes 

the scenarios that were generated for existing and expected solar installations, totaling 1.9 

gigawatts (GW), and for five future scenarios with an additional 500-1,000 MW. 

Section 3 documents the methods used to convert the hourly data set irradiation values to 

minute-to-minute power outputs. Section 4 presents the statistical analysis of one year’s worth of 

power variability data on multiple timescales. Single-plant, regional, and state-wide aggregations 

will illustrate the smoothing effects of spatial diversity.

Section 5 places the analysis of solar power variability in the context of Gujarat electric grid 

operations and planning. The Gujarat State Load Dispatch Centre (SLDC) is ultimately 

responsible for maintaining the load-generation balance. Together with GETCO, the SLDC has 

taken many steps to accommodate and anticipate grid challenges caused by the variable and 

uncertain nature of solar and wind power. This section examines how GETCO and the SLDC 

could use the solar variability profiles in operations and planning. 

Finally, as discussed in Section 6, it is recognized that solar profiles are only part of the 

renewable energy integration study needs. The broader context calls for similar evaluation of 

wind power deployment scenario variability. Only studies of the full, integrated electric system, 

including load variability, will allow system planners and operators to examine and address 

challenges imparted by large-scale variable generation futures.
4

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2 Solar Power Scenarios

The PV solar power scenarios used in this analysis are divided into two groups: existing and 

expected locations, and expansion locations. The existing and expected locations include 1.9 GW 

of direct current (DC) capacity and are called the “baseline” scenario. The expansion locations 

add 500 to 1,000 MW of additional DC capacity. All scenarios are planned to meet a target DC 

installed capacity. The algorithm for transferring DC panel output to alternating current (AC) 

power output from the inverter is explained in Section 3.15

Figure 1 shows the average annual global horizontal irradiance (GHI) for India. The state of 

Gujarat has a high annual average solar resource of 5.5 to 6.0 kilowatt-hours (kWh)/square 

meters/day over the entire state. This uniformity allows solar power plants to be optimally 

located based on proximity to load centers and transmission lines.

 

15 Direct current (DC) ratings refer the capacity of the photovoltaic panels under standard conditions: 1,000 

watts/square meter, 25°C. Alternate current (AC) ratings refer the peak power output of the inverter, which includes 

system losses and DC to AC conversion losses.
5

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Figure 1. Annual average of GHI for India

2.1 Existing and Expected Locations

The existing and expected locations form the baseline scenario. This baseline scenario has three 

cases, which vary by extent of fixed-tilt versus tracking: 1) all locations have fixed-tilt PV plants, 

2) 10% of the capacity is installed as single-axis tracking systems (with the remaining 90% 

installed as fixed-tilt systems), and 3) 25% of the capacity is installed as single-axis tracking 
6

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systems (with the remaining 75% installed as fixed-tilt systems).16 It is unknown what proportion 

of the existing and expected PV locations are or will be single-axis tracking plants. In the United 

States, approximately 30%-50% of new utility PV plant installations are single-axis tracking.17

Figure 2 shows the grid cells (approximately 10 kilometers [km] by 10 km) where the PV plants 

in the 100% fixed-tilt baseline scenario are located. These locations are based on information 

from GETCO. The GETCO maps of existing (see Figure A-1) and expected (see Figure A-2) PV 

plant locations can be found in the appendix. The baseline scenarios with 10% and 25% singleaxis tracking are shown in Figure 3a and 3b, respectively.

Figure 2. Baseline scenario PV plant locations in Gujarat, India18,19

 

16 All fixed-plate systems modeled for Gujarat, India, were assumed to be mounted at a 17° tilt and oriented due 

south (azimuth of 180°). Tracking systems were modeled as single-axis tracking systems with a tilt of 0° and 

oriented with a north-south pivot axis. 17 Ong, S.; Campbell, C.; Denholm, P.; Margolis, R.; Heath, G. Land-Use Requirements for Solar Power Plants in 

the United States. NREL/TP-6A20-56290. Golden, CO: National Renewable Energy Laboratory, 2013. 

http://www.nrel.gov/docs/fy13osti/56290.pdf. 18 The power map reflects infrastructure as of March 2013. More recent maps can be found at 

http://getco.co.in/getco_new/pages/power%20map.php. 19 The excluded area is used to inform PV siting in one expansion scenario, and comprises forests, permanently 

flooded areas, regularly flooded areas, urban areas, water bodies, slope >5%, and protected areas.
7

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Figure 3. Baseline scenarios with approximately 200 MW of single-axis tracking (a) and 

approximately 450 MW of single-axis tracking (b)

2.2 Expansion Locations

To anticipate how variability might change if Gujarat continues to pursue renewable energy 

growth targets, the authors created solar profiles for five additional scenarios reflecting different 

locations for PV plants. Three of the sites for future scenarios (Charanka, Kutchh, and over the 
8

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Narmada Canal) were suggested by GETCO staff. The other two scenarios were selected to 

reflect broader geographic diversity: solar capacity allocations across seven central station 

locations throughout the state and a distributed rooftop scenario across 16 cities. The latter was 

selected to reflect the growing interest in India for rooftop PV.20 Including the baseline, eight 

scenarios and their profiles were produced and analyzed. The remainder of this section will 

describe the five expansion scenarios in greater detail.

2.2.1 Charanka

The “Charanka” scenario expands the baseline scenario by adding 1,000 MWDC at the Charanka 

Solar Park. One thousand MW requires about 25 square kilometers (km2), which is one-quarter 

of a grid cell. Figure 4 shows the location of the Charanka scenario (yellow) on the baseline 

scenario map (orange). The Charanka Solar Park was initially started in 2010 and will be located 

on more than 20 km2 near Charanka in the Patan district in north Gujarat. The solar park was 

envisioned to be the site of 500 MWDC of installed solar capacity and is expected to be 

completed by the end of 2014. GETCO is supporting the project with infrastructure upgrades, 

including a 400-kilovolt transmission line, to evacuate the solar power produced.21 This 500 

MWDC capacity is reflected in the baseline scenario. The addition of 1,000 MWDC in the 

expansion scenario would result in a total of 1,500 MWDC of solar capacity at the Charanka 

Solar Park. 

This is the only scenario that includes solar expansion at an existing site. While future solar 

growth might occur at other existing sites, the Charanka scenario was chosen to capture the most 

centralized expansion and is useful in illustrating the impact of concentrated deployment on 

variability.

 

20 “Gujarat prepares new solar rooftop policy.” PV Magazine, Sept. 9, 2013. www.pvmagazine.com/news/details/beitrag/gujarat-prepares-new-solar-rooftop-policy_100012661/#axzz2t8RFTuNM. 21 “Report on Green Energy Corridors: Transmission Plan for Envisaged Renewable Capacity.” Power Grid 

Corporation of India Ltd., July 2012. www.forumofregulators.gov.in/Data/study/Report-Green-Energy-Tr.-

corridor.pdf. 
9

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Figure 4. Charanka scenario is 1.0 GW of additional capacity at Charanka Solar Park (yellow) with 

the baseline scenario (orange)

2.2.2 Seven Utility Photovoltaic Locations

The “seven utility PV locations scenario” expands the baseline scenario by adding about 143 

MWDC at each of seven locations throughout western Gujarat. Figure 5 shows the locations of 

the seven PV plants. The seven locations are based on grid cells with the highest annual GHI

intensity and at least 4 km2 (using 40 MWDC/km2

) of developable land. Developable land 

excludes forests, permanently flooded areas, regularly flooded areas, urban areas, water bodies,

22

slope >5%, and protected areas.

23 Developable land within grid cells is also tested for 

proximities, including: 5 km from roads24 and 3 km from transmission.

25 If two adjacent gridcells qualify, the next best grid cell is used in order to diversify the locations of the seven utility 

PV plants. 

 

22 “Global Land Cover Characterization: Eurasia Version 1.” U.S. Geological Society, 2008. Version 1: 

http://edc2.usgs.gov/glcc/glcc.php. 23 “ProtectedPlanet.net” United Nations Environment Programme and International Union for the Conservation of 

Nature, 2014. www.protectedplanet.net/. 24 “OpenStreetMap.” OpenStreetMap contributors, undated. Accessed Jan. 7, 2013: 

http://www.openstreetmap.org/#map=5/51.500/-0.100. 25 Shah, M.N. “Power Map of Gujarat” sent in personal communication to Stoltenberg, B., National Renewable 

Energy Laboratory. GETCO, May 27, 2013. The map was labeled as Updated March 31, 2013. 
10

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Figure 5. Seven utility PV locations scenario (yellow) each with 143 MW PV plants with the 

baseline scenario (orange)

2.2.3 Kutchh Region

The “Kutchh region” scenario is six utility PV plants spread through the Kutchh region, as 

shown in Table 1. The allocation of capacity throughout Kutchh was performed under the 

guidance of GETCO.

26 Grid cell selection within an area was performed using the same 

guidelines of annual GHI intensity and developable land use restriction outlined in Section 2.2.2.

Table 1. Allocation of Capacity for Utility PV Plants in the Kutchh Region

Area Capacity (MWDC)

Bhachau and Shivlakha 200

Anjar 200

Varsana 200

Nakhatrana 150

Abdasa 150

Nani Khakhar 100

Figure 6 shows the grid cells selected for the Kutchh scenario and the baseline scenario.

 

26 Kalsaria JE, D.H. Email to Stoltenberg, B., National Renewable Energy Laboratory. GETCO, Sept. 30, 2013. 
11

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Figure 6. Kutchh scenario (yellow) consists of six utility PV plants ranging from 100 to 200 MW 

with the baseline scenario (orange)

2.2.4 Narmada Canal

The “Narmada Canal” scenario is based on recent reports that fixed PV panels are being 

mounted over the canal.

27,28 The selection of grid cells for this scenario was formed by finding 

the grid cells that spatially intersect the canal and calculating the PV capacity available within 

each grid cell, assuming 1.3 MWDC per kilometer of canal. The grid cells are sorted by annual 

GHI intensity, and the grid cells are selected in order of highest resource quality until the 

cumulative sum is 500 MWDC. 

Figure 7 shows the grid cells selected along the Narmada Canal. The average DC capacity per 

grid cell is 31 MW.

 

27 “Gujarat’s canal-top solar power plant: 10 must-know facts.” The Economic Times, April 10, 2013. 

http://economictimes.indiatimes.com/slideshows/infrastructure/gujarats-canal-top-solar-power-plant-10-must-knowfacts/slideshow/19472958.cms. 28 “Gujarat lights up canal with 10 MW project.” PV Magazine, Aug. 14, 2013. www.pvmagazine.com/news/details/beitrag/gujarat-lights-up-canal-with-10-mw-project_100012392/#axzz2ojoILPCO.
12

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Figure 7. Narmada Canal scenario (yellow) with 500 MW distributed along the Narmada Canal in 

Gujarat with the baseline scenario (orange)

2.2.5 Sixteen Cities: Rooftop PV

The “16 cities: rooftop PV” scenario consists of 62.5 MWDC of rooftop PV in 16 large cities in 

Gujarat, for a total of 1,000 MWDC. The cities are randomly selected from a spatial data set29 that 

identified large cities. Rooftop PV is assumed to be evenly distributed over the entire grid cell. 

Figure 8 shows the locations of the 16 cities on top of the baseline scenario. Grid issues for 

rooftop deployment include distribution system concerns related to safety and electric effects. 

Specific local feeder impacts depend on interconnection voltage, protection schemes, inverter 

characteristics, and other factors of feeder system design. For the purpose of this paper, only the 

aggregate power ramping effects are examined, with the view of characterizing changes in the 

bulk electric system operational needs. This scenario captures the impact of the most widely 

dispersed geographic deployment.

 

29 “ESRI Data and Maps 9.3: World Populated Places.” ESRI, 2005. Cities: Vadodara, Dhrangadhra, Patan, 

Visnagar, Gandhi Dam, Dahod, Nadiad, Anand, Jamnagar, Botad, Bhavnagar, Porbandar, Kundla, Ukal, Mahuva, 

and Veraval. www.esri.com/.
13

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Figure 8. Sixteen cities: rooftop PV scenario (yellow) consists of 62.5 MW in each of 16 cities with

the baseline scenario (orange)

2.3 Summary of Scenarios

Table 2 summarizes the capacity, number of locations, geographic area encompassing all 

locations, and scenario density for the baseline scenarios and each of the expansion scenarios, 

independent of the baseline scenario. The geographic area and the scenario density serve as a 

proxy for estimating the geographic diversity. In addition, the densities of the baseline plus each 

expansion scenario were computed.

Table 2. Capacity, Area, and Density of the Baseline and Expansion Scenarios for Gujarat

Scenario Capacity of 

Baseline or 

Expansion

(MWDC)

Number 

of Plant 

Locations

Scenario 

Area 

(km2

)

Scenario 

Density 

(MWDC/km2

)

Baseline plus 

Expansion 

Scenario Density 

(MWDC/km2)

Baseline (all scenarios) 1905 84 72604 0.026 -

Charanka 1000 1 1000 1.000 0.040

Seven utility PV plants 1000 7 28652 0.035 0.039

Kutchh region 1000 6 3884 0.257 0.040

Narmada Canal 497 16 7199 0.069 0.032

16 cities: rooftop PV 1000 16 62813 0.016 0.032
14

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3 Sub-Hour Solar Power Data

Rapid changes in PV power output at a single location are observable in the time series shown in 

Figure 9, an example from Boulder, Colorado. These rapid changes in solar power output may 

impact the operation of other generators and the economics of operating the grid reliably.

30 Solar 

power production data with high temporal and spatial resolution is needed in order to study the 

effect of different solar capacity expansion scenarios. The qualities of the data set, most relevant 

to an integration study, include:

31

• Solar power data must be time synchronized to the weather conditions during each time 

step and at each geographic location.

• Solar power data must have sufficient temporal resolution to capture site-specific solar 

power output ramps.

• Solar power data must have appropriate spatial-temporal correlations to capture intraplant and plant-to-plant ramping correlations.

• Solar power data must have sufficient geographic resolution to represent the relative solar

power injection into the power system at each location.

 

30 Lew, D. et al. How do Wind and Solar Power Affect Grid Operations: The Western Wind and Solar Integration 

Study. Proceedings of the 8th International Workshop on Large-Scale Integration of Wind Power and on 

Transmission Networks for Offshore Wind Farms, Bremen, Germany, Oct. 14–15, 2009. NREL/CP-5500-54684. 31 Hummon, et al. Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis,

Preprint. Proceedings of the 2nd International Workshop on Solar Power in Power Systems, Nov. 12-13, 2012. 

NREL/CP-6A20-60548.
15

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Figure 9. Simultaneous irradiance measurements from two sites 70.4 km apart at two time scales:

tstep = 1 minute (line) and satellite irradiance values, tstep = 60 minutes (dotted line with circles). The 

site near Boulder, Colorado, is located at 39.911_N -105.235_W (gray), and the site near Golden, 

Colorado, is located at 39.742_N -105.180_W (black).32

The following subsections describe the source of the hourly irradiance and meteorological data 

for Gujarat, summarize the method for downscaling hourly irradiance data to one-minute 

irradiance data, and describe the conversion of the irradiance and meteorological data to power 

output from the solar plant.

3.1 Satellite Irradiance and Meteorological Data

The new historic 10-km solar radiation data set for India capturing hourly insolation values for 

2002-2011, including GHI and direct normal irradiance (DNI), is derived from satellite images 

from the Meteosat satellite using the semiempirical model developed by Perez et al.33 The data 

set captures impacts of earth/solar geometry and localized atmospheric effects, including clouds, 

aerosols, humidity, and decreased visibility. The data is mapped into a 10 km by 10 km grid on 

the Earth’s surface. The irradiance data is shifted from Greenwich Mean Time to local standard 

time.34 The hours listed in Table 3 replicated the same value over the entire region. During these 

hours, the sub-hour irradiance algorithm, which analyzes and categorizes the spatial variability,

did not perform as expected because the standard deviation of the identical spatial data is 

undefined.

35

 

32 Hummon, et al. Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis,

Preprint. Proceedings of the 2nd International Workshop on Solar Power in Power Systems, Nov. 12-13, 2012. 

NREL/CP-6A20-60548. 33 Perez, R. et al.. "A New Operational Satellite-to-Irradiance Model." Solar Energy (73:5), 2012; pp. 307-317. 34 Wilcox, S. M. National Solar Radiation Database 1991-2010 Update: User's Manual. NREL/TP-5500-54824.

Golden, CO: National Renewable Energy Laboratory, 2012. www.nrel.gov/docs/fy12osti/54824.pdf. 35 These hours were not included in the statistical variability analysis in Section 4.
16

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Table 3. Hours of Missing Meteosat Satellite Data

Day Hours (local 

time)

Dec. 8, 2006 3 p.m.

Dec. 11, 2006 3 p.m.

Dec. 14, 2006 2 p.m.

Dec. 16, 2006 2 p.m., 3 p.m.

Dec. 24, 2006 4 p.m.

Dec. 30, 2006 2 p.m.

Dec. 31, 2006 3 p.m.

Meteorological data, including dry bulb temperature and wind speed, necessary to calculate the 

power output from a PV module is from the National Aeronautics and Space Administration 

Modern-Era Retrospective Analysis for Research and Applications data set.

36 The data set is 

available to the public through NREL

at http://rredc.nrel.gov/solar/new_data/India/nearestcell.new.cgi.

3.2 Sub-Hour Irradiance Algorithm

This study uses the sub-hour irradiance algorithm (SIA), developed for the U.S. Western Wind 

and Solar Integration Study Phase 2.

37 The algorithm generates synthetic GHI values at an 

interval of one minute, for a specific location, using satellite-derived, hourly irradiance values for 

the nearest grid cell to that location and grid cells within 40 km. The input to SIA, spatial 

irradiance data surrounding the location of interest, make the output of SIA dependent on the 

specific location, season, and time of day by classifying the likely temporal variability from the 

spatial distribution of irradiance variability. During each hour, the observed GHI value for the 

grid cell of interest and the surrounding grid cells is related, via probability distributions, to one 

of five temporal cloud coverage classifications. An algorithm for each cloud coverage 

classification was designed to reproduce the variability statistics of that class in the form of a 

time series with one-minute time steps.

3.2.1 Spatial and Temporal Variability Relationship

The algorithm predicts the temporal variability from a spatial “patch” of satellite data points. The 

GHI values are converted to a fraction of the clear sky irradiance value, called the clearness 

index (ci), to remove the diurnal effects of the solar zenith angle. Figure 10a shows the measured 

GHI from a ground sensor and the predicted clear sky GHI. Dividing the measured GHI by the 

clear sky GHI results in a number between zero and one, where one is clear sky conditions and 

zero is no visible irradiance, as shown in Figure 10b. Clearness index values greater than one 

occur both due to cloud-focusing events, where irradiance is reflected off of the edge of a cloud,

and thus, the total irradiance at the earth’s surface is greater than the clear sky transmittance path, 

and where the clear sky irradiance estimate is underestimated.

 

36 For more information, see: http://rredc.nrel.gov/solar/new_data/India/about.html. 37 Lew, D. et al. Western Wind and Solar Integration Study Phase 2. NREL/ TP-5500-55588. Golden, CO: National 

Renewable Energy Laboratory, 2013. www.nrel.gov/docs/fy13osti/55588.pdf. 
17

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Figure 10. GHI for the ground-measured data and calculated for the clear sky on Feb. 24, 2008, at 

NREL’s Solar Research Radiation Laboratory in Golden, Colorado (a). Dividing the observed GHI 

by the clear sky GHI results in the clearness index (b).

The satellite hourly data provide a snapshot of the sky at that location. The single snapshot of 

irradiance has a correlation coefficient of 0.669 with the mean ci of the next 60 minutes of 

measured, ground data, while the distance-weighted mean of satellite ci data from approximately 

40 nearby locations have a correlation coefficient of 0.736. This set of time-synchronized ci

satellite data representing an area of approximately 4,500 km2 is called a “patch.” Figure 11

shows an example of the patch data on the left of each pair of figures and the measured ci time 

series on the right from the location marked on the patch. At hour 13, the spatial data plot shows 

a fairly clear sky, and the time series hovers near a ci of one, with three brief clouds depressing 

the ci to 0.3. By hour 16, the spatial data is much darker (average ci of 0.42), and the time series 

hovers near 0.5 with three to four brief periods of clear sky. Measures of the variance (σ), mean

(μ), and statistical distribution of ci values in each patch, characterized at each hour, form the 

basis for the probability statistics relating patch data and temporal data.

Figure 11. Spatial clearness index patch (a) and time series measured at the starred location for 

the same hour of time (b). Data shown are from the Measurement and Data Instrumentation Center 

at NREL, in Golden, Colorado.
18

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The one-minute ci data is categorized into five classifications of cloud cover (Classes I-V). 

Visual inspection of 60 consecutive one-minute values of ci, grouped by the mean and standard 

deviation of the clearness index over that period, yielded classification of irradiance variability 

into one of five classes, as shown in Figure 12. Classes I, II, and III show relatively low 

variability, with less than 0.02%, 0.87%, and 4.53% of one-minute ramps exceeding 0.1 ci, 

respectively. Classes IV and V demonstrate distinct, sharp shifts in ci with greater than 20% of 

one-minute ramps exceeding 0.1 ci. Class V irradiance variability is distinct from Class IV not 

by the magnitude of ramps (in fact, the portion of Class IV ramps between 0.05 and 0.2 exceeds 

Class V), but by the physical interpretation of the temporal data. Class V represents clear sky 

conditions with intermittent clouds, while Class IV irradiance variability is characterized by 

multiple ci states. The temporal classification scheme is based on inferential statistics. By 

grouping the 60-minute periods by mean and variance, an inductive inference from a subset of 

observations is able to be made to the parameters of the whole group.

Figure 12. Classes of temporal variability (from SIA)

3.2.2 Modeling the Temporal Variability Classes

The steps in producing synthetic temporal variability ci values are outlined in Figure 13. First, 

the clear sky GHI is calculated for every location within 40 km of the location of interest. The set 

of locations surrounding the site of interest is called a patch. Second, the mean and standard 

deviation of the patch data, for a given hour, selects a temporal classification probability 

distribution.

Figure 13. Workflow for producing modeled ci values at a single location
19

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Figure 14 shows three examples of patches and the temporal variability classification probability 

distribution, which is selected based on the weighted mean and standard deviation of the ci

values in the patch. 

Figure 14. Weighted mean and standard deviation of the patch ci values determines the 

probability distribution of temporal classifications for that patch
20

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A random number draw against the probability distribution for temporal variability class 

determines which algorithm is run to model the one-minute interval ci values. The algorithms for 

Classes I, II, and III are similar in method, only differing in the distribution of ramps (see Figure 

15) used to generate the sequential values of modeled ci. Classes I, II, III, and Class 0 have a 

slowly changing ci, where the change in ci from one minute to the next is independent of the

mean ci. For these classes, 60 random draws from the ci ramp distribution are cumulatively 

summed from the ci value at the beginning of the hour. A slope is applied to the time series 

determined by the mean slope between the ci value at the beginning of the hour and the ci value 

at the beginning of the next hour.

Figure 15. Clearness index ramp distributions for measured data with temporal variability 

classification of Class 0, I, II, and III

The algorithms for Classes IV and V are similar in concept, though they are based on different 

qualities of the patch data set. Classes IV and V are modeled on the basis that the cloud cover in 

the sky and the resulting ci classification can be characterized as a system undergoing transitions 

between states. The states represent different opacities of cloud cover, and the transition 

probabilities represent cloud movement over the area. Class IV is modeled as a system with the 

probability of transitioning between six ci states defined as the range of ci: 0.01-0.3, 0.31-0.45, 

0.46-0.6, 0.61-0.75, 0.76-0.9, and 0.91-1.1. The probability of the state is determined by the 

distance-weighted proportion of the ci values from surrounding sites in that ci state. The state 

duration is determined by a random draw from a distribution of state durations, which were 

derived through empirical analysis of state durations in measured data.

Class V is a simpler system, consisting of just two states: clear (ci >0.9) and cloudy (ci <0.75). 

The cloudy or clear state ci value is determined by finding the distance-weighted mean of ci

values, such that ci <0.9 or ci >0.9, respectively. The transition probability of moving from a 

clear to cloudy state is determined by the distance-weighted proportion of sites that are cloudy 

compared to the total number of distance-weighted sites. The transition probability of returning 

to a clear state is determined by drawing a random cloudy state duration from empirical analysis, 

similar to Class IV.
21

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The algorithms for each temporal class act on one-hour intervals to produce one-minute synthetic 

ci values. Before steps five through seven are executed (see Figure 13), the one-hour sets of ci

values are concatenated into one-time series. The remaining subsections describe the reduction in 

variability with increasing PV plant area and the conversion of irradiance to AC power output.

3.2.3 Reduction in Variability with Increasing Photovoltaic Plant Area

Power output from large PV plants may be less variable, relative to the installed capacity, than a 

small PV plant because the power output from a PV plant is the average of the power output of 

all the modules. Modules that are spread out over a large area do not necessarily experience the 

same power output variability (e.g., cloud movement) at the same time. The synthetic oneminute GHI values from SIA represent the variability of a single photosensor, about 1 square 

centimeter in area. To model the variability of a real PV plant, a technique developed by Marcos 

et al.38 was used, which represents the reduction in variability as a low-pass filter with a cut-off 

frequency dependent on the spatial area of the PV plant. Figure 16 shows the time series 

representing a point source and the filtered time series for a PV plant with an area of 2.6 km2

.

Figure 17 shows the cut-off frequency, displayed as the cut-off time, below which the variability 

is reduced as a function of the (a) PV plant area and the (b) DC capacity, assuming the packing 

density is 40 MW per square kilometer. For instance, a 1-km2 PV plant has a reduction in 

variability below eight minutes, while a 4-km2 plant shows a reduction in variability below 15

minutes. 

Figure 16. Time series of one-minute GHI before and after applying the low-pass filter for a plant 

area of 2.6 km2

 

38 Marcos, J. et al. “From irradiance to output power fluctuations: the PV plant as a low pass filter.” Progress in 

Photovoltaics (19:5), 2011; pp. 505–510. http://onlinelibrary.wiley.com/doi/10.1002/pip.1063/pdf. 
22

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Figure 17. Cut-off time below which there is reduced variability due to the geographic area of the 

plant

3.2.4 Converting Irradiance Data to Alternate Current Power Output

A one-minute power time series is calculated from the synthesized one-minute solar irradiance 

data (GHI, diffuse irradiance [DFI], and DNI) using the PVWatts program.

39 PVWatts models

PV solar power generation by modeling the sun position, plane of array irradiance, module 

cover, thermal effects, and inverters. The inputs to the program are time, DNI, DFI, wind speed 

at 10 meters above the surface, temperature, system DC capacity, derate, the tracking mode 

(fixed, single–axis, double–axis), and tilt and azimuth angle of the installed PV. PVWatts outputs

are the incident plane of array irradiance, transmitted plane of array irradiance, the output DC 

power, and the output AC power. All fixed-plate systems modeled for Gujarat were assumed to 

be mounted at a 17° tilt and oriented due south (azimuth of 180°). Tracking systems were 

modeled as single-axis tracking systems with a tilt of 0° and oriented with a north-south pivot 

axis. The modeled derate is 0.81 and is a single number that “represents losses in the system due 

to nameplate direct current (DC) ratings, inverter efficiency at maximum power, mismatch, 

diodes and connections, DC wiring, alternating current (AC) wiring, soiling, system availability, 

shading, tracking error, and aging.”40 An inverter efficiency curve reduces the AC power output 

at sub-maximum DC power from the photovoltaic panel. While different PV panel technologies 

have different conversion efficiencies, the authors chose to use an efficiency equivalent to 

crystalline silicon modules for all locations.41 The authors did not attempt to capture differing 

efficiencies for either existing/planned locations or postulated future installations.

 

39 Dobos, A. P. PVWatts Version 1 Technical Reference. NREL/ TP-6A20-60272. Golden, CO: National Renewable 

Energy Laboratory, 2013. www.nrel.gov/docs/fy14osti/60272.pdf. 40 Dobos, A. P. PVWatts Version 1 Technical Reference. NREL/ TP-6A20-60272. Golden, CO: National Renewable 

Energy Laboratory, 2013. www.nrel.gov/docs/fy14osti/60272.pdf. 41 Ref. Dobos 2013 describes the module efficiency in terms of a reference module output at 1,000 watts/square 

meter with -0.5% coefficient for each degree of cell temperature deviation from the reference cell temperature of 

25°C.
23

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3.3 Summary

The authors synthesized one-minute interval solar power output for locations in Gujarat based on 

the spatial variability observed in the satellite data for 2006. The PV plant characteristics include 

installed DC capacity, tracking and panel orientation, as well as a general derate that 

encompasses losses due to wiring, shading, and aging, and a DC to AC conversion efficiency 

curve. Modeled data reflect installation characteristics, including utility-scale PV plants based on 

land-use requirements and rooftop PV panels that cover wide areas. The power production and 

variability of the scenarios is evaluated in Section 4.
24

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4 Solar Power Variability

The addition of new solar in Gujarat would affect system operations, for example, in how much 

conventional generation must increase or reduce supply to match changes in electric load and 

solar output, such as from sunrise and cloud coverage. Nevertheless, the Gujarat SLDC can 

prepare for these impacts by analyzing the likely power fluctuations of solar, including: diurnal 

and seasonal power fluctuations; timescales of change (e.g., from one to 30 minutes); magnitude 

of change (both total megawatts and relative to installed capacity); and extent to which the 

changes can be anticipated in advance. A single year of sub-hour data captures the seasonal and 

time of day variability; however, multiple study years would be needed to understand the longterm economics of a PV plant at a particular location. This section describes such an analysis, 

applied to both the baseline and expansion scenarios, as well as the sum of the baseline (all fixed 

panel) plus the expansion scenarios. This section first reviews the absolute power production, 

followed by variability at 15-minute, five-minute, and one-minute intervals.

4.1 Scenario Power Production and Capacity

The baseline scenarios include both existing and planned PV plants. The study developed three 

scenarios, all with a total nameplate capacity (DC) of 1.9 GW: 100% fixed-axis, flat-panel PV 

modules; 90%/10% fixed-axis/single-axis tracking PV modules; and 75%/25% fixed-axis/singleaxis tracking PV modules (see Section 2). 

Table 4 summarizes the capacity, annual generation, and capacity factor for the three baseline

scenarios. The substitution of 10% or 25% of the nameplate capacity of fixed-axis PV plants 

with single-axis tracking increases the annual power output by 2% and 5%, respectively, which 

increases the capacity factor as well. Each of the baseline scenarios has 84 PV plant locations, 

with an average nameplate capacity of 22.7 MW. The smallest plant is 1 MW, and the largest 

plant is 500 MW. Table 4 also shows the AC capacity and AC capacity factor for each scenario. 

The AC capacity was determined by taking the sum of the peak AC power output of each 84 PV 

plants, under clear sky conditions.42 The AC capacity factor increases from 24.3% for the 

baseline scenario with all fixed-PV systems to 25.5% for the baseline scenario with 25% singleaxis tracking.

 

42 There is no established method for determining the rated AC capacity. One possible method is to use the AC 

rating of the inverter; however, the power transfer function used in this study, PVWatts, uses the full DC rating of 

the panel as the inverter rating and then applies a derate to the DC power output that encompasses system losses and 

an inverter efficiency curve. This study used the peak AC power output from the clear sky simulation to estimate the 

maximum AC capacity capable by the system.
25

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Table 4. Installed Capacity, Annual Power Output, and Capacity Factor for the Expected Location 

Scenariosa

Scenario

Annual Power 

Output (MWh)

AC 

Capacity 

(MW)

AC 

Capacity 

Factor

DC 

Capacity 

(MW)

DC 

Capacity 

Factor

Baseline 2,834,226 1330 24.3% 1905 17.0%

Baseline with 10% 

single-axis tracking 2,897,073 1335 24.8% 1905 17.4%

Baseline with 25% 

single-axis tracking 2,984,858 1337 25.5% 1905 17.9%

a Insolation data is from 2006.

Figure 18 shows the power output for a PV plant in the Kutchh region (200 MWDC) configured

as either a fixed-axis system with a tilt of 17° or a single-axis tracking system with a tilt of 0° on 

Jan. 23-24, 2006. January is close to the winter solstice (late December), when the sun appears to 

be furthest south. During midday, the fixed system has a higher power production because the 

panel’s tilt of 17° off the horizontal reduces the angle of incidence between the sun’s rays and 

the surface of the PV panel. The single-axis tracking system has steeper sunrise and sunset slopes

because the tracking panel decreases the angle of incidence at these extreme zenith angles and 

therefore produces more power. The baseline scenarios with 10% and 25% tracking capacity 

show a moderate increase in sunrise and sunset ramps (see Section 4.2).

Figure 18. Power output for a 200 MWDC PV plant with either fixed panel at 17° tilt or a single-axis 

tracking panel with no tilt, on Jan. 23-24, 2006, at 23.65° N, 69.55° E in the Kutchh region.

The “expansion” scenarios include new PV plants that may or may not be proposed or planned. 

The five scenarios include (1) adding 1.0 GWDC at the Charanka Solar Park, (2) adding seven 

utility-scale PV plants with a total installed capacity of 1.0 GWDC, (3) adding 1.0 GWDC in the 

Kutchh region, (4) siting 500 MWDC of fixed-panel PV over the Narmada canal, and (5) adding 

rooftop PV in 16 cities with a total installed capacity of 1.0 GWDC. Results are presented for 

both the expansion scenarios, independently, as well as the sum of each expansion scenario with 

the baseline scenario with all fixed-axis panels. The expansion scenarios have a very similar DC 

and AC capacity factor to the baseline scenarios. When the expansion scenarios are combined 

with the baseline scenario of all fixed-plate PV plants, the AC capacity factor ranges from 24.2% 

to 24.5%. These capacity factors are more indicative of the meteorological conditions in Gujarat 
26

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in 2006 than of the particular scenarios. Ten or more years of data (daily or hourly insolation) is 

typically used by developers and financiers to estimate the long-term expected performance of a 

PV plant.

Table 5. Installed Capacity, Annual Power Output, and Capacity Factor for the Expansion 

Scenarios and the Baseline Scenario (all fixed-axis) Plus Each Expansion Scenarioa

Scenario

Annual 

Power 

Output 

(MWh)

AC 

Capacity 

(MW)

AC 

Capacity 

Factor

DC 

Capacity 

(MW)

DC 

Capacity 

Factor

Charanka 1,454,356 670 24.8% 1,000 16.6%

Utility PV at 7 Locations 1,519,665 702 24.7% 1,000 17.3%

Kutchh 1,514,560 702 24.6% 1,000 17.3%

Narmada Canal 737,452 353 23.8% 497 16.9%

Rooftop PV in 16 Cities 1,498,158 716 23.9% 1,008 17.1%

Baseline + Charanka 4,288,582 2,000 24.5% 2,905 16.9%

Baseline + Utility PV at 

Seven Locations 4,353,891 2,032 24.5% 2,905 17.1%

Baseline + Kutchh 4,348,786 2,032 24.4% 2,905 17.1%

Baseline + Narmada 

Canal 3,571,678 1,683 24.2% 2,402 17.0%

Baseline + Rooftop PV in 

16 Cities 4,332,384 2,046 24.2% 2,905 17.0%

a Insolation data is from 2006.

Figure 19 shows the time series for a single site (left column) and the aggregate of all baseline 

scenarios (right column) for two days, July 23, 2006, and Oct. 12, 2006 (top row and bottom 

row, respectively). The clear-sky PV power output is shown as a reference for the expected PV 

power output from the PV plant if there were no clouds in the sky. In July (see Figure 19a), the 

monsoon season, the time series from individual PV sites often show characteristics of clouds in 

the sky—rapid changes in power output due to clouds obstructing the irradiance path from the 

sun to the PV module. Actual PV power output can exceed the clear sky PV power output during 

intermittent conditions due to cloud focusing. The clear sky PV power output is drawn as a band 

because the expected PV power output when there are no visible clouds present varies from day 

to day and site to site due to different densities of aerosols and water vapor in the atmosphere.43

The calculation of clear sky values for the sub-hour irradiance algorithm can be found in 

Hummon et al.

44 The aggregate time series on July 23 (Figure 19c) shows significant reduction 

 

43 The clear sky estimate accounts for multi-day reduced visibility from air pollution. 44 Hummon et al.

44 Sub-Hour Solar Data for Power System Modeling From Static Spatial Variability Analysis,

Preprint. Prepared for 2nd International Workshop on Solar Power in Power Systems, Nov. 12-13, 2012. NREL/CP6A20-56204. Golden, CO: National Renewable Energy Laboratory, 2012. www.nrel.gov/docs/fy13osti/56204.pdf.
27

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in total power output compared to the clear sky conditions, as well as a decrease in the rapid 

changes in power output due to the geographic diversity of the baseline scenario.

Figure 19. Clear sky and synthetic PV power output, one-minute resolution, for (left) single, 

randomly selected site from the baseline PV scenario, and (right) aggregation of all of the baseline 

PV sites on July 23, 2006, and Oct. 12, 2006

In October, the aggregate time series power nearly matches the clear sky power output in all of 

the baseline scenarios. Single locations can have significant midday variability (Figure 19b);

however, the variability of a 5-MWDC PV plant has little effect on the aggregate performance of 

1,905 MWDC plants spread over a 72,000 km2 area. 

The aggregate power output from the baseline scenario, each expansion scenario (separate from 

the baseline), and the baseline scenario plus each of the expansion scenarios are shown in Figure 

20 for both July 23, 2006 (monsoon season), and Oct. 12, 2006 (dry season). Figure 20 (top left) 

demonstrates that during the monsoon season the largest variability in solar power output among 

the expansion scenarios is from the Charanka scenario, in which PV is concentrated at a single 

location. Three expansion scenarios (rooftop PV in 16 cities, seven utility PV locations, and 

Kutchh region), when combined with the baseline scenario, all show a decrease in the relative 

magnitude of unpredicted ramps on July 23 due to an increase in the total geographic diversity 

(the aggregate magnitudes are of course most important to grid systems operation). During the 

dry season, geographic diversity is not as important because there are “clear skies” over nearly 

all of Gujarat.
28

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Figure 20. Aggregate power output in the baseline and expansion scenarios, separately (top) and 

combined (bottom), on July 23, 2006 (left) and Oct. 12, 2006 (right)

The contrast of the time series between July and October is apparent in Figure 21, which shows 

the power production from the baseline (all fixed panel) scenario by time of day and day of year. 

The data has been averaged to 15 minutes on the vertical axis and three days on the horizontal 

axis. Peak power production is at 1:00 p.m. local time on average, with the highest months of 

power production being March through May. Monsoon season is visible in June, July, and 

August, with a decrease in power production of approximately 25% to 50%.
29

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Figure 21. Color map of power production from the baseline scenario by time of day (vertical axis) 

and time of year (horizontal axis)

4.2 Variability of the Scenarios

The magnitude of solar power generation variability will depend on the penetration of solar, 

geospatial diversity, and weather patterns in the region. Figure 22, which shows the distribution 

of five-minute clear power index ramps, shows how geographic diversity of a PV scenario can 

decrease the relative variability. The clear power index is the synthetic five-minute solar power 

output divided by the clear sky five-minute power output. The baseline scenarios, with differing 

percentages of single-axis tracking, do not change the distribution of five-minute clear power 

index ramps appreciably. The expansion scenarios with the lowest variability are the rooftop PV 

in 16 cities and the Narmada Canal. These two scenarios have high geographic diversity and high 

resource quality, both of which decrease the frequency of solar power ramping events. Section 

4.2 presents multiple measures of solar power variability, including the absolute solar power 

ramps, clear power index ramp, and unpredicted short duration ramps.
30

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Figure 22. Five-minute clear power index ramp distribution for each baseline scenario and each 

expansion scenario (separate from the baseline). The “count” axis is on a log scale.45

The frequency of a particular “larger ramp magnitude” increases with increasing PV penetration, 

while the number of smaller ramps decreases. This is seen in Figure 23a, where the number of 

ramps in the 35 to 50 MW per five-minute range is nearly two times greater for the baseline plus 

expansion scenarios (mean count = 1,337 across scenarios), with a total installed capacity of 2.9 

GWDC compared to the baseline scenario alone (count = 638). The same is true for 15-minute 

ramps (see Figure 23b). The baseline plus Narmada scenario is slightly smaller (2.4 GWDC), and 

thus, the increase in frequency of larger ramps, from 970 to 1,517 counts, is limited to 35 to 40 

MW per five-minute range. The other baseline plus expansion scenarios show an increase in the 

frequency of ramps of 60 MW per five minutes. The frequency of ramps greater than 88 MW per 

five minutes is less than 0.10% for all scenarios.46 That threshold is reached at 80 MW per five 

minutes for the rooftop PV and seven utility PV location scenarios.

 

45 The clear power index (cpi) is the ratio of the power production to the power production that was expected in the 

absence of any clouds (clear sky conditions). This is similar to the clearness index that was introduced in Section 3 

(see Figure 10). The five-minute cpi ramp is the difference between the cpi at time t = 00:10:00 and the cpi at time t

= 00:05:00.

46 0.10% is equivalent to less than 100 intervals of five minutes.
31

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Figure 23. Absolute five-minute (a) and 15-minute (b) solar power ramp distribution for the 

baseline scenario and the baseline scenario plus each of the five expansion scenarios (daytime 

analysis)

The distribution of clear power index ramp values is shown in Figure 24 for a single site from the 

baseline scenario located near Bhabhar, Gujarat, and for the aggregate of the baseline scenario 

and the baseline plus each expansion scenario. The baseline scenarios show a significant 

reduction in the number of ramps that are greater than 5% of the clear power production 

compared to the single site.

Figure 24. Distribution of five-minute clear power index ramps for the baseline scenario, baseline 

plus expansion scenarios, and a randomly selected site from the baseline scenario (1 MW fixedPV plant located at 24.05°, 71.55°)
32

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Figure 25 shows the five-minute clear power index ramp as a function of both time of day 

(vertical) and day of year (horizontal) for the expansion scenarios of (a) Charanka and (b) 

rooftop PV in 16 cities (these do not include the baseline scenario). The higher variability in 

Charanka, throughout the rest of the year, is due to the lack of geographic diversity of the 

Charanka scenario. When the baseline scenario is summed with the expansion scenarios, the 

variability is greatly reduced (see Figure 24c and 24d), though baseline plus Charanka still 

appears to have more variability during the monsoon season than the baseline plus rooftop PV in 

16 cities.

Figure 25. Comparison of the daytime variability of three scenarios: baseline, Charanka, and 

rooftop PV in 16 cities
33

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Figure 26. Definitions for (a) time of day and (b) season for five-minute ramp analysis

To explore the seasonal and time of day changes in solar power variability, the authors use

Figure 26 to define the time of day analysis (Figure 26a) and the dry and monsoon season 

analysis (Figure 26b). Distributions of five-minute ramps are studied to determine trends in 

variability over time and scenario. There are six measures of five-minute ramps used in this 

analysis. The first three are the median absolute value ramp (50th percentile) for a scenario by 

season and time of day, and that ramp is expressed as a percent of installed DC capacity or as a 

percentage of the clear power ramp. The second three are the 95th percentile absolute value ramp 

for a scenario by season and time of day, and again, that ramp scaled by capacity and clear sky 

ramp. For example, Figure 27a shows the distribution of absolute value five-minute ramps for 

the baseline scenario during sunrise and midday. The median (50th) and 95th percentile ramp are 

marked on each distribution. Figure 27b shows the same ramps, scaled by the installed capacity 

(1,900 MWDC for the baseline scenario and 2,900 MWDC for the baseline plus Charanka 

scenario) so that the ramp is now expressed as a percentage of installed capacity. Lastly, Figure 

27c shows the five-minute clear power index ramp for the baseline scenario during midday for 

the dry and monsoon seasons.
34

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Figure 27. Example of the distribution of five-minute ramps and the identification of the 50th and 

95th percentile: (a) the baseline scenario, in the dry season, for the distributions of five-minute 

absolute ramps for the sunrise and midday "time of day" periods, (b) the distributions of fiveminute absolute ramps scaled by the installed capacity for the baseline and baseline plus 

Charanka scenarios during midday of the dry season, and (c) the baseline scenario during 

midday, for the dry and monsoon seasons, expressed as a percentage of the clear sky five-minute 

ramp.
35

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Table 6. The Median (a) and 95th Percentile (b) Five-Minute Ramp (absolute magnitude, percent of 

DC solar capacity, and clear power index) by Season

(a) Median Five-Minute Ramp (daytime hours, excluding sunrise and sunset; see definition of 

daytime in Figure 26)

Monsoon Season Dry Season

Scenario Ramp 

(MW)

Ramp 

(% of DC 

Capacity)

CPI 

Ramp 

(%)

Ramp 

(MW)

Ramp 

(% of DC 

Capacity)

CPI 

Ramp 

(%)

Baseline (1.9 GW) 14.9 0.8 1.0 16.0 0.8 0.5

Baseline with 10% Tracking (1.9 

GW) 14.6 0.8 1.0 15.4 0.8 0.5

Baseline with 25% Tracking (1.9 

GW) 14.1 0.7 1.0 13.9 0.7 0.5

Baseline + Charanka (2.9 GW) 22.7 0.8 1.0 24.2 0.8 0.5

Baseline + Kutchh (2.9 GW) 22.2 0.8 0.9 24.0 0.8 0.5

Baseline + Narmada Canal (2.4 

GW) 17.9 0.7 0.9 19.9 0.8 0.4

Baseline + Rooftop PV in 16 

Cities (2.9 GW) 21.2 0.7 0.9 24.0 0.8 0.4

Baseline + Utility PV at 7 

Locations (2.9 GW) 22.6 0.8 0.9 24.1 0.8 0.4

(b) 95th Percentile Five-Minute Ramp (daytime hours, excluding sunrise and sunset; see 

definition of daytime in Figure 26)

Monsoon Season Dry Season

Scenario Ramp 

(MW)

Ramp 

(% of DC 

Capacity)

CPI 

Ramp 

(%)

Ramp 

(MW)

Ramp 

(% of DC 

Capacity)

CPI 

Ramp 

(%)

Baseline (1.9 GW) 43.6 2.3 4.7 37.6 2.0 2.9

Baseline with 10% Tracking (1.9 

GW) 45.3 2.4 5.3 38.4 2.0 3.3

Baseline with 25% Tracking (1.9 

GW) 45.3 2.4 4.7 38.0 2.0 3.2

Baseline + Charanka (2.9 GW) 71.8 2.5 5.1 58.0 2.0 3.0

Baseline + Kutchh (2.9 GW) 59.1 2.0 4.1 55.7 1.9 2.5

Baseline + Narmada Canal (2.4 

GW) 47.8 2.0 4.0 45.9 1.9 2.6

Baseline + Rooftop PV in 16 

Cities (2.9 GW) 53.8 1.9 3.8 55.3 1.9 2.4

Baseline + Utility PV at 7 

Locations (2.9 GW) 57.5 2.0 3.9 55.6 1.9 2.5

Table 6 tabulates all of the measures of five-minute ramps for each baseline scenario and the 

baseline (all fixed axis) plus each expansion scenario that are illustrated in Figure 27. The ramps 

in Table 6 do not include sunrise and sunset (see Figure 26 for the definition of time of day). The 

95th percentile ramp represents the largest change in the solar power production that a system 

operator can expect during 95% of the five-minute periods. The substitution of fixed-axis plants 
36

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for single-axis tracking plants increases the size of the 95th percentile ramp during both the 

monsoon and dry seasons, while decreasing the size of the median ramp. The largest absolute 

magnitude five-minute ramps are found in the baseline plus Charanka scenario because it has 

both a larger installed DC capacity and a higher geographic density than the baseline scenario. 

The magnitude of the ramps scaled by the installed DC capacity shows the Charanka scenario 

(without the baseline scenario) has 64% and 55% greater 95th percentile ramp magnitude than the 

baseline scenario during the monsoon and dry seasons, respectively. This is because the 

Charanka scenario is a single location, while the baseline scenario has PV plants spread across an 

area greater than 72,000 km2. The five-minute ramps in the monsoon season expressed as a 

percentage of the clear power ramp are about twice that of the dry season measured at both the 

50th and 95th percentiles.

Figure 28 shows the 95th percentile ramps relative to the installed capacity of the scenario as a 

function of time of day and season. During the dry season (Figure 28a), across all scenarios, the 

largest ramps occur during sunrise and sunset. The peak solar output is 25% to 50% greater in 

the dry season than in the monsoon season and that peak is reached over a very short period of 

time; hence the largest ramps occur during sunrise and sunset. The sunrise ramp is exacerbated in 

the baseline scenarios with single-axis tracking (not shown). The baseline with 25% tracking 

scenario has a 95th percentile five-minute ramp, as a percentage of installed capacity, of 4%. 

The smallest ramps occur during the midday of the dry season, when there are very few clouds.

The monsoon season (Figure 28b) shows a different time of day pattern than the dry seasonthe 

largest ramps occur during the midday, when there are many clouds that change the solar power 

production. The baseline plus Charanka scenario stands out as being distinctly more variable in 

the monsoon season than the other baseline plus expansion scenarios. 

Figure 28. The (a) dry season and (b) monsoon season 95th percentile five-minute ramp (percent of 

scenario installed DC solar capacity) during each time of day: sunrise, morning, midday, 

afternoon, and sunset
37

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In contrast to the 95th percentile five-minute ramps, which present the extreme ramps over five 

minutes, the average “unpredicted” one-minute ramp is characterized by how similar the system 

is from one five-minute period to the next. The calculation of “unpredicted” one-minute ramps

used is illustrated in Figure 29. The “unpredicted” ramp is the difference between the projected 

and actual one-minute solar power data (Steps 2 and 3 of Figure 29).47 This is calculated in the 

clear power index space to minimize the effect of the zenith angle of the sun. The unpredicted 

ramps are expressed as a change in solar power megawatts per minute (Step 4 in Figure 29).

The average unpredicted down (decrease in solar power) and up (increase in solar power) 1-

minute ramps are shown in Figure 30. The data is divided into dry and monsoon seasons because 

the monsoon season has nearly twice the absolute magnitude of unpredicted one-minute solar 

power ramps. The baseline scenario is shown on the far left, and the incremental addition of 

ramps from the expansion scenario is shown as a darker set of blocks below/above the baseline 

down/up ramps. The Narmada Canal scenario has a smaller magnitude of one-minute ramps 

because the expansion scenario is 500 MW, compared to the 1,000 MW in the other expansion 

scenarios. Across all of the remaining baseline plus expansion scenarios, the additional 

unpredicted one-minute ramps seems to be driven more by total installed capacity than 

geographic diversity.

Figure 29. Illustration of the calculation of unpredicted one-minute solar power variability

 

47 The calculation and interpretation of unpredicted one-minute ramps is the subject of ongoing research.
38

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Figure 30. Average minute-to-minute change in solar production, normalized by expected clear 

sky solar production for the baseline scenario plus each expansion scenario. Darker tones reflect 

the incremental addition of ramps from the expansion scenarios.

4.3 Summary

Section 4 explores the absolute power production and variability of the baseline scenario (all 

fixed axis), each expansion scenario, and the baseline plus each expansion scenario. The analysis 

demonstrates that ramping increases with solar capacity, dominating any difference due to 

geographic diversity—baseline (1.9 GWDC) ramping is less than baseline plus Narmada Canal 

(2.4 GWDC) ramping, which is less than the ramping in other baseline plus expansion scenarios 

(2.9 GWDC). As Figure 27a illustrates, the five-minute ramps as a percentage of installed DC 

solar capacity are relatively constant across the baseline and expansion scenarios during the dry 

season, but vary based on time of day.

The effect of geographic diversity becomes significant when cloud coverage contributes to solar 

ramping. In this case, geographically diverse locations, such as baseline plus utility PV at seven 

locations or rooftop PV, which are represented by relatively low power densities, experience 

lower absolute ramps than similarly sized scenarios. The baseline plus Charanka scenario has 

30% and 7% greater daytime ramps in the monsoon and dry seasons, respectively, over the 

baseline plus rooftop PV in 16 cities.

The greatest difference between the monsoon and dry seasons are seen in the average 

unpredicted one-minute ramps, which demonstrates that persistence predictions in the dry season 

are more accurate than in the monsoon season.
39

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These measurements of variability, relative to the installed capacity, clear sky ramps, and 

absolute magnitude of ramps, give system planners and operators a better understanding of how 

each expansion scenario will impact system operations.
40

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5 Actions to Address the Variability of Solar 

Generation

The variability of solar generation, as described in the previous section, can be managed through 

changes to system operations and planning, a number of which are already being adopted in 

Gujarat. The changes most relevant to Gujarat span the areas of grid reinforcement, system 

operations, and market reforms. 

5.1 Grid Reinforcement

One potential constraint to increasing solar generation is the capacity of the grid. A strong grid 

serves two key purposes:

• Power can be physically evacuated from areas with high solar resources to where the 

electricity is needed

• System operators can access a broader range of flexible resources, including conventional 

generation, to balance the variability of net load (which is electricity demand minus the 

electricity supplied by wind and solar).

To identify grid infrastructure priorities necessary to meet renewable energy targets in the 12th

five-year plan, the central transmission utility, the Power Grid Corporation of India Ltd., 

conducted a “green corridors” study.

48 The engineering study optimizes inter- and intrastate 

transmission capacity expansion based on likely areas of renewable energy development as 

identified by state agencies in seven states, including Gujarat, with input from central agencies,

such as the Ministry of New and Renewable Energy, Central Electricity Regulatory Commission, 

Central Electricity Authority, and Power System Operation Corporation. The proposed 

infrastructure is designed to both move electricity between states and help resource-rich states 

better absorb renewable energy within the state, while maintaining both frequency and voltage 

stability.

49

The green corridors report identified several infrastructure improvements for Gujarat, including 

increased interstate transmission capacity to better connect with the northern region and evacuate 

power from the Charanka Solar Park, intrastate lines to better integrate wind and solar generation

internally, and dynamic volt-ampere reactive compensators and other equipment needed to 

maintain voltage. This transmission will facilitate access to solar power for out-of-state 

customers, currently a critical constraint for renewable energy capacity expansion. System 

operators will also be able to better manage solar ramping, as identified in the previous section, 

with additional transmission capacity to resources that can help balance (provided mechanisms 

exist to enable resources, especially from out-of-state, to respond to this variability). 

 

48 “Report on Green Energy Corridors: Transmission Plan for Envisaged Renewable Capacity.” Power Grid 

Corporation of India Ltd., July 2012. www.forumofregulators.gov.in/Data/study/Report-Green-Energy-Tr.-

corridor.pdf. 49 Although the transmission corridors are being planned, commercial arrangements to secure the grid upgrades have 

yet to be implemented.
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Potential follow-on analysis to this paper, as part of a larger grid integration study for Gujarat, 

includes evaluating the impact of grid upgrades on the ability of Gujarat SLDC to manage solar 

ramps. Such an analysis would support an optimized cost-benefit assessment of transmission and 

generation investments to better evaluate scenario expansion options explored in this paper. 

5.2 System Operations

In addition to increased transmission capacity, changes to system operations—through advanced 

forecasting, improved scheduling, and ancillary services—can also help manage the variability 

of solar generation. Many of these changes will be implemented as part of the new Renewable 

Energy Management Centres (REMC), the first of which is under construction in Gujarat. The 

green corridor report proposed the REMCs, to be colocated in load dispatch centers, address 

some of the challenges to system operators in managing variable resources.

Challenge REMC Functions

Low visibility of renewable energy

forecasting, especially for solar

Forecast renewable energy generation at timescales 

ranging from hour-ahead to month-ahead, including at 

solar power generation plants

Low availability of real-time 

generation data

Provide real-time tracking of renewable energy 

generation and system conditions, including geospatial 

visualization

Limited ability to automatically 

control generation output, such as 

through a supervisory control and 

data acquisition (SCADA) system or 

automatic generation control 

Automated coordination with load dispatch centers to 

manage variability of generators to control output, when 

necessary

5.2.1 Advanced Forecasting

The load dispatch center in Gujarat receives forecasting data (day ahead and real time) for all 

solar plants 5 MW and above, and for wind generators that are 10 MW or greater and connected 

to a pooling station.

50 The REMC in Gujarat will allow the creation of forecasts based on 

weather sensors being installed at wind locations of at least 50 MW, which will provide data on

speed, direction of wind flow, and temperature, among other factors. Because persistence 

forecasts for the next 15-minute time period are fairly accurate (for wind generally, and for solar 

in the dry season), this real-time power production data will be particularly helpful to the SLDC 

in evaluating upcoming scheduling requirements.

With better visibility of forecasted and real renewable energy generation, system operators will 

be better equipped to manage ramping. For example, in the baseline plus Charanka scenario, the 

modeling in this study shows how solar variability changes over the course of a year, and that the 

 

50 Wind plants commissioned on or after May 3, 2010, and covered under the Renewable Regulatory Fund also must 

provide forecasting data.
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monsoon months of June through August, with its cloud coverage, create the greatest variability 

in solar ramps. The REMC will be designed to predict specific storm events, allowing system 

operators to prepare for sudden changes in output. Section 4 demonstrates that dry season 

variability conditions (one-minute interval changes in solar power output) persist for longer 

periods than monsoon season variability conditions. In other words, it is harder to predict shortterm variability in the monsoon season than in the dry season. Although the practice and science 

of very short-term solar forecasting is evolving, the forecasting objective of the REMC will 

likely not address the unpredicted one-minute ramps. An ancillary service, such as automatic 

generation control, would likely offer the most value to the SLDC in managing unexpected

ramping.

Nevertheless, improved forecasting will allow system operators to better anticipate periods of 

ramping and adjust the quantity of operating reserves based on expected system conditions. This 

paper illustrates two components of solar variability. The first is determined by the solar path, 

and plant location and configuration. The associated morning ramp-up and evening ramp-down 

power changes are known in advance, and can therefore be anticipated in day-ahead scheduling. 

The second, cloud-driven component of variability becomes better anticipated through 

forecasting, and is diminished by the geographic diversity of broad deployment scenarios. For 

example, this study shows that reserves needed to manage midday solar variability are likely to 

be minimal in October, when skies are largely clear, and greatest in July, when solar variability 

is less predictable. 

5.2.2 Improved Scheduling

Unlike some other states in India, Gujarat has a strong base of resources to meet demand and 

hold reserves. The adequacy of resources provides Gujarat more in-state options to balance wind 

and solar variability. Access to this flexibility, however, is limited if schedules are fixed dayahead and offer little ability to make intraday adjustments based on improved forecasts. 

One mechanism to access flexibility is exercising direct control. Under the Energy Act of 2003, 

the SLDC functioned primarily as a coordinator rather than a controller of resources. The Indian 

Electricity Grid Code of 2010 now permits direct management of identified resources through 

automatic generation controls. However, in practice, in Gujarat and all other Indian states, use of 

governors has been limited, in part due to the prevalence of large frequency swings. With the 

progressive narrowing of the frequency band, the use of automatic generation controls has 

become a possibility. As noted in the description of the REMC functions, the REMC will be 

designed to feed forecast and generation information into the SLDC scheduling system. This

allows the SLDC to use real-time telemetry, such as through software like SCADA, which is 

used to transfer data and control equipment remotely, to directly control the output of wind and 

solar generators.
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Thermal plants offer another source of flexibilityelsewhere, coal has demonstrated the ability 

to cycle on and off up to twice a day.51 Coal-based generators in India—both publicly and 

privately ownedare wary of schedule revisions, given the maintenance requirements and wear 

and tear associated with cycling. However, these costs (e.g., wear and tear while ramping and 

providing ancillary services), along with information on operating parameters (e.g., fuel 

availability, ramp rate, minimum generation point), can be incorporated in economic dispatch 

when revising intraday schedules. Gujarat is already using coal as a source of flexibility to help 

balance variability. The state has secured agreement from NTPC Ltd. to vary coal generation 

under Gujarat’s contractual control (500 MW), from 60% to 100% of unit capacity.52 Expanding 

the use of thermal plants to meet intraday scheduling revisions will provide greater options for 

Gujarat to balance variability and address uncertainty.

Automation of scheduling at state load dispatch centers will also help system operators balance 

ramp events. Currently, many scheduling transactions are not automated—SLDCs can require 

several hours to clear requests from state distribution utilities and over a day from captive 

(industry-owned) power plants. This manual process, in turn, impacts the speed of scheduling at 

regional and national load dispatch centers. Once all scheduling is conducted online, as already 

occurs in Gujarat, state-level clearance can be automated, which in turn, will increase for all 

states the liquidity and access to resources to help balance net load.

5.2.3 Ancillary Services

The development of ancillary services would be one of the most significant changes to system 

operations, with significant potential to improve options and reduce the costs of managing 

variability and uncertainty. Ancillary services would compensate for the provision of reserves, 

tailored to different time scales, and voltage control. This package is in consideration by the 

Central Electricity Regulatory Commission,

53 and would likely replace system operators’ 

existing tool for balancingthe unscheduled interchange mechanism.

54 Although Gujarat has a 

greater reserve margin for peak demand relative to other states (500-1,000 MW, provided by 

conventional generation), ancillary services could be structured to ensure adequate spinning (hot) 

reserves, which are currently in insufficient supply.55

The type, magnitude, and timing of ancillary services can be tailored to meet the projected 

variability, and its uncertainty, of renewable energy. The analysis in Section 4 illustrates the 

 

51 Cochran, J.; Lew, D.; Kumar, N. “Flexible Coal: Evolution from Baseload to Peaking Plant.” 21st Century Power 

Partnership. NREL/BR-6A20-60575. Golden, CO: National Renewable Energy Laboratory, 2013. 

http://www.nrel.gov/docs/fy14osti/60575.pdf. 52 Large-Scale Grid Integration of Renewable Energy Sources—Way Forward. Central Electricity Authority, 2013. 

www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf. 53 “Introduction to Ancillary Services in Indian Electricity Market.” Central Electricity Regulatory Commission 

(CERC), April 10, 2013. www.cercind.gov.in/2013/whatsnew/SP13.pdf. Note CERC staff is currently revising this 

paper to evaluate a wider range of options, beyond markets, for SLDCs to procure ancillary services.

54 The unscheduled interchange mechanism prices deviations from schedule such that there is a financial incentive to 

maintain grid frequency. The mechanism limits how much schedules are allowed to deviate.

55 Integrating Variable Renewable Energy with the Grid: Lessons from the Southern Region. Mercados Energy 

Markets India PVT Ltd., November 2012. 

www.shaktifoundation.in/cms/uploadedImages/variable%20re%20grid%20integration.pdf. 
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types of ancillary services of most importance to solar variability. For example, in the monsoon 

season, automatic generation control would be valuable to address the unexpected minute-tominute variations due to cloud coverage. The analysis in Section 4 also illustrates potential 

magnitudes, for example, in the magnitude of solar output change over five-minute periods. The 

study finds that greater than 88 MW ramps per five minutes occurs less than 0.10% of the time 

for all scenariosthis type of information could serve as input for reserve capacity calculations. 

Varying the timing of ancillary servicessuch as dynamic reserves that are targeted to ramping 

needs specific to time of day and yearcan reduce the cost of integrating variable renewable 

energy. This paper demonstrates the change in variability and uncertainty over the course of the 

day and across a year. When solar variability is combined with wind and load data, including 

forecast errors for all three, the net variability can be calculated, and an evaluation can be made 

on how to define and schedule reserves, and whether the capacity to provide these reserves is 

sufficient among existing generators.

Finally, this paper also demonstrates the impact of geographic diversity on reserve management, 

and quantifies the differences in cloud-driven ramping between extremely centralized 

deployment (Charanka), alternative widely distributed central plants (utility PV at seven 

locations), and even broader distributed rooftop scenarios (rooftop PV in 16 cities). Rooftop PV 

would require fewer reserves to manage solar ramping compared to other expansion scenarios.

5.3 Market Reforms

Commercial impediments to renewable generation sales remain one of the greatest challenges in 

Gujarat for scaling up renewable energy. As with other states, utilities in Gujarat must pay a 

feed-in tariff for wind and solar production (average of rupees 3.56/kWh in June 2012), and must 

balance excess supply by backing down cheaper generation (rupees 2.50-2.70/kWh).56 Other 

states may be deterred from purchasing Gujarat’s renewable energy, for a number of reasons:

• States have limited options to balance variability. The state purchasing solar through a 

power purchase agreement (PPA) is also purchasing the balancing requirement.

• Energy purchased through the unscheduled interchange during periods of supply excess, 

such as during the windy monsoon months, is cheaper than establishing PPAs.

• Obligations for other states to meet renewable energy targets are not enforced.

Changes to market design and participation, such as development of a robust spot market, would 

increase the options for system operators to address solar ramping, providing a centralized 

exchange to balance wind and solar variability. Also, contracts for all generators (e.g., to provide 

compensation for part load, flexible operations, and ancillary services) will likely require

renegotiation to align with evolving operating and market structures. Wholesale market designs 

for energy and ancillary services can help integrate and make efficient the provision of these 

services, and serve as a framework for contract renegotiation.

 

56 Large-Scale Grid Integration of Renewable Energy Sources—Way Forward. Central Electricity Authority, 2013. 

www.cea.nic.in/reports/powersystems/large_scale_grid_integ.pdf.
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Short of having a robust market, improving cooperation across balancing areas can help expand 

access to resources needed for balancing, although this is less of an issue for Gujarat, which has 

adequate in-state resources for balancing. Typically in India, the process for revising the dayahead interstate schedules is inflexible. As forecasts for generation and load increase with 

accuracy close to real-time power delivery, system operators have limited options to revise 

schedules. For example, an intraday market on the power exchanges exists, but with too little 

participation to offer liquidity. The SLDCs have limited options to revise intraday, interstate 

schedules with the regional load dispatch center, for example, to accommodate a bilateral trade 

necessary for balancing. Therefore, SLDCs have three primary options to balance their system in 

real time: adjust schedules of in- and out-of-state resources (such as conventional generation 

based on merit order), shed load (in Gujarat, agricultural feeders are scheduled to be curtailed 

first), and purchase or sell power through the unscheduled interchange mechanism). 

Options to improve interstate trade could include improved intraday market liquidity, sharing of 

reserves (such as under development in the Western U.S. energy imbalance market),57 and 

increased availability to revise interstate schedules.

5.4 Summary

Targeted changes to system operations and planning can account for anticipated impacts on grid 

operations introduced by increased amounts of solar generation, across all scenarios analyzed. 

Because most of the solar ramping is based on known changes due to sunrise and sunset, the 

Gujarat SLDC can schedule its resources accordingly. The greater periods of uncertainty stem 

from cloud-based variability. To address this impact, Gujarat is in the process of strengthening 

its suite of tools to anticipate and mitigate this variability, including stronger grids, geographic 

diversity of PV locations, advanced forecasting (day ahead and real time), improved scheduling, 

introduction of ancillary services, and strengthened markets across balancing areas. 

 

57 Milligan, M.; Clark, K.; King, J.; Kirby, B.; Guo, T.; Liu, G. Examination of Potential Benefits of an Energy 

Imbalance Market in the Western Interconnection. NREL/TP-5500-57115. Golden, CO: National Renewable 

Energy Laboratory, 2013. www.nrel.gov/docs/fy13osti/57115.pdf. 
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6 Summary, Implications, and Next Steps

Higher penetration levels of wind and solar add variability and uncertainty to power system 

operations. To assess the adequacy of balancing resources, and to evaluate operational practices 

to access these resources, system operators and planners typically perform grid integration 

studies. Key to informative analysis is accurate representationspatially and temporallyof the 

power variability and uncertainty of solar and wind generation. This report focuses on one 

element of a grid integration studysolar variability.

This paper demonstrates the use of a new Indian solar radiation data set and a method to develop 

sub-hourly power variations. The insolation profiles are used to derive potential PV power 

generation profiles for additional deployment locations in the state of Gujarat. This work brings 

out several salient features that are important when evaluating potential future PV renewable 

generation and its potential grid integration impacts on the power system, including:

• The diurnal and seasonal power profiles and variability statistics for the existing and 

planned 1.9 gigawatts (GW) of solar are characterized using 2006 historic solar resource 

data. The geographic spread of this baseline is relatively broad. Power output is raised 

slightly if a portion of the plants is assumed to incorporate single-axis tracking, but the 

statistics of variability are largely unaffected by tracking. 

• The expansion scenarios of 500 to 1,000 MW increase nearly proportionally the absolute 

magnitude of the solar variability. The differences in geographic density of the expansion 

scenarios affect variability, but when combined with the geographically diverse baseline 

scenario and normalized to the total nameplate DC capacity, the differences among 

scenarios are small. 

• Results illustrate the power ramp rate differences between a single small plant, a very 

large plant, several spread out central plants, and rooftop distributed deployments. The 

least geographically diverse, baseline plus Charanka scenario has 30% and 7% greater 

midday ramps in the monsoon and dry seasons, respectively, over the more 

geographically diverse baseline plus rooftop PV in 16 cities scenario.

• In evaluating expansion plans, system planners need to consider other factors related to 

location, such as access to uncongested transmission capacity, in addition to variability 

differences. If solar deployment across more Indian states is considered, and state-to-state 

grid cooperation is increased, the beneficial impacts of geographic diversity should be 

larger and would be worth re-evaluating.

• During the monsoon season, individual plant variability can be quite large due to cloud 

passage, but the aggregated power across all plants shows less volatility. Due to the broad 

and general nature of the storms, planning and operating the system using a derating of 

the solar capacity may help systems operations. Further research could assess whether

this implication could be applicable to other regions of the world with similar monsoonal 

climatology and where solar power is being deployed or contemplated.

• The study shows that much of the diurnal solar variability is based on a known solar path, 

illustrating a large amount of the PV-imposed needs for grid flexibility are known in 

advance, which facilitates grid operational day-ahead scheduling.
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• The variability shows strong seasonal characteristics. Generally, absolute variability is 

higher during dry seasons during known sunrise and sunset ramps. Variability relative to 

output is higher during the monsoon season at midday, when the region experiences a 

dramatic decrease in solar power output due to periodic (and relatively unpredictable) 

cloud passage. 

• Diurnal and seasonal variability characteristics can be important to grid practices. Known 

variability implies ramping resources can be confidently quantified and potentially 

scheduled. Unpredictable variability statistics can also be quantified, but may require 

partially loaded quick ramp or quick start/stop resources to respond. 

These results inform grid operations and planning processes with realistic solar variability 

characteristics applicable to various futures in Gujarat. With data, one can assess the impacts of 

increases in solar deployment and formulate approaches to address challenges.

6.1 The Need for Similar Wind Resource and Power Profiles

The availability of the new historic solar data set for India was key to motivating this PV 

variability analysis. However, wind power is currently posing more challenges to the Gujarat 

SLDC. During 2012-2013, when Gujarat’s installed wind capacity reached over 3 GW, wind 

power fluctuated more than 500 MW within 69% of the days, while fluctuating more than 1,000 

MW on 16% of the days.

Like the solar variability analyzed in this paper, similar analyses of sub-hourly wind variability 

and different potential future wind power generation scenarios are needed to more fully 

understand the challenges of increasing grid-connected renewable energy. 

Compared with solar, generating historic weather-year resource models for wind is more 

difficult, requiring use of complex mesoscale representation of the atmospheric flows. While 

models are available, they are computationally intensive, require often hard-to-find measurement 

data for calibration, and necessitate skilled parametric tuning to capture hub-height phenomena. 

The U.S-India Energy Dialog Work Plan: Joint Action Plan58 intends to address this gap. As part 

of this, the Centre for Wind Energy Technology and NREL will calibrate wind data, and NREL 

will complete the mesoscale model development in the first half of this year (2014). 

Once this model is available, development of scenario-specific wind power profiles using data 

from the historic weather year 2006 is anticipated. Wind results will be matched with the solar 

profiles developed in this study. Such contiguous weather-year PV and wind power profiles will 

provide a key element needed for grid integration operational and planning analyses, and 

complement inputs based on load profiles and conventional generator characteristics. For 

example, such an analysis could address whether system operators can maintain reliability during 

the monsoon season, when wind output is strong, the uncertainty of solar variability is highest, 

 

58 U.S.-India Energy Dialog Work Plan: Joint Action Plan, 2013. New Technology and Renewable Energy Working 

Group, December 2012. http://scholar.google.com/scholar_url?hl=en&q=http://asiasolarenergyforum.org/wpcontent/uploads/sp-client-document-manager/2/2013-03-04-ntre-action-plan---draft-final--dec2012.docx&sa=X&scisig=AAGBfm1PpebStYrcxbRbsA378sW4CgqT1Q&oi=scholaralrt. 
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and many sources of system flexibility are constrained (e.g., hydro storage is full, coal capacity 

is offline or at part load). Such analyses can then be used to anticipate future variable renewable 

challenges and evaluate possible solutions. 

6.2 Uses of Renewable Power Variability Profiles

Integration studies of the full integrated electric system, including conventional generator 

capabilities, load variability, operational practice reform, and assessment of system-level 

mitigation measures will allow system planners and operators to examine and address challenges 

imparted by large-scale variable generation futures. 

Realistic wind and solar power profiles corresponding to potential future deployment scenarios 

help characterize some of the integration challenges. Without these evaluations, vagueness and 

anxiety might otherwise hold back development. With the power profiles, many types of 

analyses can be performed, including: 

• Intra- and interregional transmission load flow and transmission expansion planning 

studies

• Evaluation of reactive power requirements and harmonics

• Production cost operational modeling of the integrated grid

• Resource adequacy-based reliability assessments, including wind and solar capacity 

credit determinations.

Performing these types of investigations leads to a better understanding of the challenges of a 

grid with high penetration levels of renewable energy. Through production cost modeling, both

physical and institutional features of the grid can be examined, for example:

• Ramp capabilities of conventional generators and possible maintenance cost increases 

due to increased cycling

• Market structure and dispatch practices

• Balancing mechanisms and associated ancillary service and reserve procedures

A holistic approach to grid integration analysis will recognize that each element might be 

important for further planning and could serve as the basis to support technology innovation, 

new procurement rules, new transmission interties, among other strategies to integrate wind and 

solar. Operational evaluations have often led to creative solutions.

Once the flexibility needs are quantified, a wide variety of mitigation approaches can be 

evaluated. Some effective approaches based on international experience include: 59 60

 

59 See, for example, Holttinen, H. et al. Design and Operation of Power Systems with Large Amounts of Wind 

Power. VTT Technology, 2013. www.ieawind.org/task_25/PDF/T75.pdf. 
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• Improving forecasting in appropriate time frames through better data gathering and 

analysis

• Shortening unit commitment and dispatch intervals and gate closures

• Alternative market structures and balancing mechanisms (ancillary services and reserves)

• Utilizing smart-grid modernization to improve situational awareness, data flow, and 

improved access to demand control measures (such as is being developed through the 

Renewable Energy Management Centre in Gujarat)

• Increased balancing area cooperation through shared imbalances, reserves, and 

load/resource diversity (for India this includes national-to-state and state-to-state 

cooperation)

• Steering future generation additions to encourage increased geographic dispersion of 

renewables and more flexible conventional generation

• State-of-the-art active power control capabilities on wind and solar plants, such as ramp 

rate control, inertial response, and automatic generation control.

These types of analyses and evaluations of solutions are predicated on realistic, quantified wind 

and solar power variability profiles, such as what is illustrated in this paper for solar PV 

generation in the state of Gujarat. 

 

60 Cochran, J., L. Bird, et al. Integrating Variable Renewable Energy in Electric Power Markets: Best Practices 

from International Experience. Golden, CO, National Renewable Energy Laboratory, 2012. 

www.nrel.gov/docs/fy12osti/53732.pdf.
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Appendix: Solar Plant Diagrams

Figure A-1. Existing solar plants in Gujarat
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Figure A-2. Expected solar plants in Gujarat